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Hyperspectral Image Analysis - Science topic

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Hi I'm looking for some mineral and rock spectra repositories to use in the interpretation of remote sensing images and spectral mapping for mineral prospecting and research.
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Hi, I am currently willing to work in Hyperspectral image classification. I am a beginner in the research domain. Please Anyone suggests narrow down the latest trends suitable for further work. Please help me in that matter.
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Hyperspectral Document Image processing for Writer Identification
Ink Mismatch Detection using Hyperspectral Document Imaging
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I have to process hyperspectral data which is in .he5 format. So, I am unable to open in Erdas Imagine, Arcgis, Qgis and ENVI
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AN update to my response:
As the PRISMA datasets are available, one can use ENVI 5.6 (if available) for processing PRISMA data. Apart from that ENMAP toolbox in QGIS, EarthBit software from Planetek ITALIA and ERDAS tool for PRISMA ( inbuilt in the latest version), prismaread (https://ranghetti.github.io/prismaread/index.html) can also be explored.
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I have hyperspectral data of Indian pines and the ground truth related to that. Now I want the spectral signature(information) of the material present in the Indian pines. I have tried searching the standard libraries like NASA's JPL but some materials more precisely plants like alfalfa are not present in there. can anyone help?
indian pines source
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Did you get the spectral signature of the ground truth of the Indiana pines data set.
please share it with me. I will be highly grateful to you.@
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I've come across a couple of publicly available hyperspectral image datasets from the website http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes
Now, my concern is regarding whether or not we can use these datasets directly for building machine learning based models.
Are these datasets geometrically and atmospherically correct? Or do these datasets further require atmospheric and geometric corrections prior to building any machine learning based model?
Also, how can one be assured that a captured hyperspectral image scene is atmospherically and geometrically correct?
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As Mr Nasir Hameed suggested, if you are looking forward to inspecting individual spectral signatures in a hyperspectral image, the Digital Numbers (DN) are more than adequate to design an autonomous classification, while atmospheric and geographic corrections would be unnecessary for the framework.
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Hi Everyone,
I have acquired some plant hyperspectral images (roots, fruit, leaves) from various environmental conditions and now want to explore the data cubes to detect possible differences and plan to study plant physiology and chemometrics in future. The built-in software with the camera (Specim IQ studio) is not serving the purpose.
Any suggestion for easy-to-use and simple interface software or analysis pipeline for such exploration and making classifier models? Preferably open-source but commercial suggestions are also welcomed.
Many thanks in anticipation.
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Yes, the folder looks like this for a single snap!
I will inbox you as well. Thank you again :)
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Is it possible to get Hyperspectral data for research purpose from ISRO? I am doing a research on Hyperspectral Image processing.
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How to create patches from hyperspectral image without required groundTruth data?
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I need to get a satellite image from a site in the northern part of Iran for remote sensing research usage. I wonder where I can find such data?
A friend suggested these websites, but they didn't cover 1-meter hyperspectral imagery from the expected region: "Open Aerial Map, Google Earth Pro, Airbus."
The final goal is to collect data for ecological services calculation (O2 release Co2, No2, So2 absorbance) of vegetation of the specified region. If you know a better way to doing so, also very helpful.
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Matthew Mabey Thank so much, Matthew. It's very help full. I appreciate it a lot.
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I am unable to proceed after loading the mat file. I have loaded the mat files of indian pine image and indian pine ground truth. How to proceed?
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I am trying to implement object detection using C++. I have hyperspectral data in the form of .bil file along with header file. Can anyone help me in accessing .bil files using C++?
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As I know GDAL library cab handle this data. follow below link:
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Hyperspectral medical imaging, hyperspectral image for cancer/tumor detection
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Contact Prof. Ines Gockel at Leipzig University Department of Surgery
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Can mercury pollution in waterbodies be detected using hyperspectral imagery?
Thinking along the lines of detecting evidence of gold mining.
Any feedback or paper recommendations you have would be great, thanks!
Cheers,
Sol
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Mercury pollution may be picked up by analyzing predatory fish at top of food chain. Mosquito fish (Ambrosia app.) have also been found to accumulate nitrogen. You might find added information from US National Mercury Lab. Wetlands in headwaters of black water streams have also been found to reduce mercury to toxic methyl mercury form.
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I am using autoencoder networks for deep learning based feature extraction purpose for spectral images. For the time being, the number of hidden nodes in the network is randomly chosen.
Is there any way to optimize this parameter so that the best feature representation is achieved?
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There are some practical ways to determine the best size, but it all comes to evaluations. The general rule is the optimal size of the hidden layer is usually between the size of the input and size of the output layers.
In sum, for most problems, one could probably get decent performance (even without a second optimization step) by setting the hidden layer configuration using just two rules: (i) number of hidden layers equals one; and (ii) the number of neurons in that layer is the mean of the neurons in the input and output layers.
Jeff Heaton, author of Introduction to Neural Networks in Java offers a few more.
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Is there any research papers about estimation of crop and air temperatures using thermal measurements?
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In addition to the recommended articles by Bachir Achour , you can also check out this article (see attached)
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I am working on hyperspectral image classification. I have just started my work but don't know how to load and process hyperspectral images in Matlab. Please help me.
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You can try this fcn of MATLAB image processing toolbox:
Also, you can try this 3rd party fcns:
Good luck.
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I have a hyperspectral database consisting of spectral signals between 350-1050 nm.
I calculated the simple formulas (Simple Ratio and NDVI)
But i have a big problem about creating all possible two (or three) band combinations.
I have no idea what script i should use.
Could you advice me a document which hepls me create NDVI, SR and TVI using all possible two band combinations?
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Ahmet,
Not sure you still need it, but I guess it maybe useful for others.
Please find below a small script for R. There is maybe smarter ways to write it, but at least it works (data and output attached):
#set your directory and file you want to use
setwd("D:/filepath of your working directory")
d1<-read.table("SRI_Chlab_subsampled_RG.csv", head=T, sep = ",", na.string = NA)
dim(d1)
#[1] 72 14
head(d1)
#Create an empty matrix with column and row number corresponding to the #number of wavebands
mat1<-matrix(NA, nrow = ncol(d1[,7:ncol(d1)]), ncol = ncol(d1[,7:ncol(d1)]))
colnames(mat1) <- colnames(d1[7:ncol(d1)])
rownames(mat1) <- colnames(d1[7:ncol(d1)])
#Create a subsample of the initial dataset with just the wavebands:
#number of column of this dataset = number of column the empty matrix
d2 = d1[,7:ncol(d1)]
#Loop to look over the wavebands
for(i in 1:ncol(d2)) {
for(j in 1:ncol(d2)){
if(i == j){
mat1[i,j] <- NA
}
else {
NDx = (d2[,i] - d2[,j]) / (d2[,i] + d2[,j])
#SRx = d2[,i] / d2[,j]
fit1 = lm(d1$Chlab~NDx)
mat1[i,j]<-summary(fit1)$r.squared
}
}
}
#export your data in csv format
write.csv(mat1,"R2-matrix-SRvsChlab.csv")
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hello
I need hyperspectral data for my project. how I can download free hyperspectral data Preferably with ground data?
PS: I know I can download Hyperion data from "earthexplorer" but I need ground data too.
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USGS earth explorer - https://earthexplorer.usgs.gov/ for Landsat and Sentinel2data
Experimental satellite data by registering particular access site by registering with institutional mail ID
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hello
I need to extract endmembers in a hyperspectral image, MESMA (Multiple Endmember Spectral Mixture Analysis) is a prevalent algorithm in the unmixing analysis. but I have no idea how to run it. does anybody have a tutorial video or pdf? and another question: can I conduct MESMA in ENVI software?
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Hi,
I am working on hyperspectral image analysis. I want to apply PCA on my hyperspectral dataset to reduce spectral dimensions. My question is Why do we need to center the hyperspectral data(mean subtraction) before applying Principal component analysis(PCA)? Is this step is mandatory? What will happen if we did not center the hyperspectral data before applying PCA.
When i googled about this I got following links.
From these links, I feel data centering is required to maintain same scale for all features before applying PCA. But coming to hyperspectral data cube, generally all spectral bands will be in same scale.
So, Do we need to center the hyperspectral data before applying Principal component analysis(PCA)?? What will happen if we did not center the hyperspectral data?
Thanks.
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I need to know the emissivity values of vegetation cover and water bodies from a MODIS product MOD11A2 and there are two bands 31 and 32, which one to be used and what is the difference between them both? Also, is there any better product for the emissivity?
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I would ask ask a question in line with this conversation topic. I am looking for Land Surface Temperatures at night time with satellite data that covers Europe. Modis offers some interesting data at night with a resolution of 1km, for example water vapour. On the other hand, there are some strategies to obtain LST with Landsat 8 by previously calculating the NDVI and use it in combination with the TIR band. Do you know another strategy with which I can get the result with the greatest rigor and spatial resolution as possible? Thanks in advance.
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Difference between Multispectral and hyperspectral image analysis in agriculture crops.
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Multispectral images have a very detailed and strict set of bands, while the Hyperspectral images have a lot of continues bands. The Pros and Cons really depends on what you are interested in observing.
Multispectral images are easier to use, because of the fewer amounts of bands. This means that if you can distinguish the features you are looking for using multispectral images, it makes more sense, since it will narrow down the amount of choices you have to make while using the bands.
Hyperspectral images however can detect other features that the multispectral can not, increasing the potential of making new discoveries, or connections not possible with the multispectral approach. If you can't detect specific features using multispectral images, you should try the hyperspectral images. Keep in mind that while the potential is larger, the options and choices are also more numerous, making it potentially more difficult and/or time-consuming to use.
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I am working oh hyperspectral classification images, and i need to know how to use PSO or any of the bio-inspired optimization method in such issue .
Any help ,ideas, suggestion would be much appreciated
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You can use the below Python library :
Or try to program the swarm optimization yourself... It is quite a work, but with OOP can be done in a reasonable time.
I recently did it for one of my Projects. Let me know if you would need the working python code that can be plugged with any data-set of interest.
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Hi,
There are spectra collected by asd spectroradiometer in lab environment with one source lamp on. Snapshot imaging done on the same sample with a different source lamp in the same lab environment. Extracted spectra from the snapshot images and continuum removed spectra of both asd and snapshot are compared. Although they match in absorption features, they show a certain difference in the spectra. How will I model it? Is there a way to do some quantitative analysis between the spetra of these two sensors. Attached are the images of plots showing thick lines representing asd and dashed lines representing the snapshot imager. The samples used were different beach sand samples.
Regards,
Sundara
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Can you please provide me the remote sensing sattelite data set for landcover change analysis of sundarban mangrove forest bd,india location ?
If possible then send me the prepossessed dataset in TIFF or MAT format.
You can provide me the source link..
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DigitalGlobe "open Data Program" has very good high resolution images of India check it out https://www.digitalglobe.com/opendata/flooding-in-india/pre-event
And also you can download free data sets from Copernicus scihub (SAR (Sentinel-1A) and optical Sentinel-2) and usgs earthexplorer (Landsat TM, ETM and OLI).
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I tried to plot entropy of all bands for AVIRIS dataset (Indian Pines) in MATLAB, however it shows zero value entropy for all bands. Even after scaling of the data, it shows zero value for almost all bands.
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Obviously, as per the MATLAB scaling is needed. I.e. each band image need to be scaled to 256 bins (intensity range 0-255 uint8 format), then the simple "entropy()" can be used to find the entropy. If further the problem exists, let me know, I can share the MATLAB code.
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hello
i am working with hyperspectral pixel mixtures and i want to create a netowkr that maps the abundance values of the mixtures to the spectral signatures.
Basically my input vector is 1x11 and output vector is 1x91
1x11 represents the mixture values for example [0.25,0,0,0.5,0,0,0,0.25,0,0,0] and the output label is the 1x91 spectral values or a 1x91 dimensional vector of normalized values
As you can see the input values contain many zeros and i want to create a mapping between these input mixture values to the output reflectance values.
Should i use dense layers in this case or approach it in another way?
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Hi Muneeb,
As very often, several architectures are possible to address your problem.
I suggest you the following publications for a quick but detailed overview:
Regards
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We require hyperspectral imagery data for Haryana region to carry out a preliminary analysis. If it is available with anyone kindly share the data with us or a link from where data can be acquired.
Your contribution will be highly acknowledged.
Thanks.
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Thanks and good luck
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I want to do spectral unmixing for the hyperspectral images. In one paper they have used abundance matrix for the spectral unmixing. I did not find any materiel which explains it step by step. please help me...
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We do not consider the relative spatial location, and simply classify them based on the similarity of pixels.
For example, we get a hyper-spectral image, and then we disturb the spatial distribution of pixels. We only consider the similarity of the spectra. How can I classify pixels based only on the similarity of the spectra, so that the spectral differences in the class of classification results obtained are small, and the spectral differences between the classes are very large.
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Dear Zhu,
Image segmentation whether it is multispectral or hyperspectral is an NP-problem. In other words there is not definite and decisive solution. Your request is to optimize the intra and inter clustering solution ( maximize homogeneity within each cluster and maximize heterogeneity among clusters). There are many ways to classify hyperspectral images such as Spectral Angle Mapper (SAM) [1], Modified Spectral Correlation Angle (SCA) [2], Optimized Spectral Angle Mapper (OSAM) [3], and Extended Spectral Angle Mapper (ESAM) [4]. These algorithms are based on angle measurement between actual spectral signatures and predicted ones. Moreover, some algorithms perform hyperspectral image classification using information measurement such as Spectral Divergence Information (SDI) [5]. Others combine two different approaches to classify hyperspectral images such as combining SAM and SDI [6] and Jeffries–Matusita with SAM (JM-SAM) [7].
Some are available in commercial image processing packages such as SAM, SDI in ENVI.
[1] F. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging spectrometer Data.” Remote Sensing of Environment, vol. 44, p. 145 – 163, 1993.
[2]S. Robila, and A. Gershman, “Spectral Matching Accuracy in Processing Hyperspectral Data”, in Proceedings of IEEE International Symposium on Signals, Circuits and Systems, 2005, vol. 1, pp. 163–166.
[3] L. Bertels, D. Bart, K. Pieter, D. Walter, and P. Sam, “Optimized Spectral Angle Mapper Classification of Spatially Heterogeneous Dynamic Dune Vegetation, a Case Study Along the Belgian Coastline”, in Proceedings of the 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS), Beijing, October 17–19, 2005.
[4] H. Li, W. S. Lee, K. Wang, R. Ehsani, and C. Yang, “Extended Spectral Angle Mapping (ESAM) for Citrus Greening Disease Detection Using Airborne Hyperspectral Imaging”, Precision Agriculture, vol.15, pp. 162–183, 2014.
[5] C. I. Chang, “An Information-Theoretic Approach to Spectral Variability, Similarity, and Discrimination for Hyperspectral Image Analysis”, IEEE Transactions on Information Theory, vol. 46, pp.1927–1932, 2000.
[6] Y. Du, C. I. Chang, H. Ren, C. C. Chang, J. O. Jensen, and F. M. D’Amico, “New Hyperspectral Discrimination Measure for Spectral Characterization”, Optical Engineering, vol. 43, pp. 1777–1786, 2004.
[7] S. Padma, and S. Sanjeevi, “Jeffries Matusita Based Mixed-Measure for Improved Spectral Matching in Hyperspectral Image Analysis”, International Journal of Applied Earth Observation and Geoinformation, vol. 32, pp. 138–151, 2014.
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I have a HYSI image from the NRSC-ISRO site. I want to read the GeoTiff file. I also have images from USGS.
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%File path
filepath='D:\image full path';
%Read GTiff
[image,geo]=geotiffread(filepath);
%Read info
info=geotiffinfo(filepath);
imshow(filepath)
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Is it required to normalize the reflectance with respect to one another when spectra of the same object and same lighting conditions are obtained using two different sensors?
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Thats ok. I extracted drone spectra from several neighboring pixels.
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* i have collected soil samples during hyperion pass over the study area and chemical analysis was done.
* Lab spectral signatures has not taken.
* How can i correlate the chemical analysis results to hyperion data after preprocessing?
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You need to model multi-variate regression (PLSR/PCR) between each laboratory obtained soil nutrient data of the sample points, keeping as dependent variable (Y) and spectral reflectance of the sampling points obtained from HYPERION data as independent variable (X). One usually predicts the nutrient value from the relationship between X and Y and cross-validates the predicted value through examining the precision of fit in linear regression between the actual and predicted values of nutrient.
You can learn about the MATLAB implementation of PLSR/PCR from the following link:
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Anyone knows about research-related or industrial applications of CNN for HSI data in the food science field? Unlike SVM, KNN, and other "shallow" machine learning algorithms, the CNN enables taking advantages of the spatial information of HSI data.
Most published papers deal with extraction of deep spatial features for architecture classification and other domains. But in the literature, I don't find applications of this deep learning technique on food/agricultural products!
Thank you !
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Hi,
I found this paper that maybe related with your research
”"Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network"
There are also several papers that discuss about plant disease recognition using HSI and CNN.
"Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps "
"Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field "
Hope it helps your research.
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Can compression (before classification) increased the classification acuracy?
How can we improve the classification accuracy of remote sensing image(RSI) by the help of compression?
Dear respective researchers can you please help me by providing the related articles link or your valuable opinion for these issue.
Regards
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Hello!
Yes! the careful examination/interpretation of remote sensing images by using various false color composites will help us to collect the signature of land use/land cover classes effectively.
You may refer following article for information about, how to achieve better classification accuracy.
All the best
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I am doing research in content based image retrieval using hyperspectral image. I seen many of the websites block the hyperspectral image. Can someone send me link in hyperspectral image dataset.
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You can download both multispectral as well as hyperspectral datasets from this link..
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I have a image dataset (hyperspectral) & i am using matlab as a tool. I want to implement the PCA on this hyperpspectral image dataset.
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Here is a very good source of PCA including presentation and source code Presentation Principal Component Analysis (PCA) : An Overview Article Principal component analysis - a tutorial Alaa
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While spatial, spectral and temporal advantages of multitemporal hyperspectral sounder (SAR) images provide opportunities for advancing the compression and classification techniques. So I need multitemporal remote sensing SAR images for evaluating the performance of my 4D compression techniques. Currently, I evaluate my methods by multitemporal multiapectral data.
So, if you know the source of Multitemporal SAR images then please give the link here.
Thanks & Regards
Masud Ibn Afjal
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I want to do unmixing about the hyperspectral image by NMF( Nonegative matrix factorization ), because of the nonconvexity
of the cost function C(W, H) with respect to both W and H, it is
impossible to obtain the global optimal solution. How to get a resonbled regularized term about the NMF?
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In addition to Farouk's post and my own post, more practical details about recovering the endmember basis may be found by jointly checking out:
Hoping it will be helpful to you.
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Dear Researchers,
Does anyone know where can I find a HSI database for any object or material, but with more than 25 spectral images (i.e. 25 *x*y*lambda)?
It would be nice, if somebody could propose a HSI database for agricultural, medical, food, and circuits. Thanks!
Feel free to share your database with me, I have no problem to cite your database.
Thanks in advance.
Best, Vivek
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Hi All, I tried to put all the references to the publicly available hyperspectral datasets. I hope it is of help, incase if I'm missing some, please let me know, I can update it. Thanks
best, Vivke
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I need HYDICE Urban Data Set or other similar data set for Hyperspectral anomaly detection. can any one help me? please inform me for this emergent need?
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The permanent link to the Urban HYDICE data set is:
By the way, this data looks like it's been atmospherically corrected, but certainly has spectral mismatches. Interesting that people use it unmoving when comparing to better quality measured spectral libraries.
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I tried all the conventional techniques provided in literature, but doesn't seem to be working well for required results.
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ENVI software has capabilities to find the end members. For this you have to use the PPI than n-Dimensional visualizer. That end members are matched the USGS resampled library and make your own spectral library for classification of image.
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What are the latest restrictions by the FAA on using UAVs ( fixed wing and rotary wing, e.g quadcopters) for purely scientific investigations?
Max.altitude, line-of-sight, permits, coastal and urban areas, etc.
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You can take a look at: A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs) in which different restrictions about the use of UAVs have been referenced.
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I want to use hypespectral data for mapping. Is there any remote Hyperspectral data available free or paid. anyone knows please post the links? Thank you
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You can search the EO Hyperion archive and download products on: https://earthexplorer.usgs.gov/
The satellite was decommissioned in March 2017, which means that no scenes will be addeded after that. https://lta.cr.usgs.gov/ALI
I also find the scene extents of roughly 40x7 km quite unfortunate...
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Hyperspectral Image Analysis - Two Assumptions:
1. Existence of Pure Pixel
2. Absence of pure pixel (Presence of mixel pixels alone)
Moreover, mixed Pixed - Can use either Linear Mixture Model/ Non-Linear Mixing Model  for unmixing.
My query is:
  - Pure Pixel Assumption : Does it comes under Hyperspectral Unmixing problem?
  -whether  Xi = Sai + ni => Linear Mixing Formula has to be considered for Pure Pixel Assumption problems.
Kindly clarify my doubts.
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I am not at all performing classification. My concentration is on identifying pure pixels and finding the endmember of those pure pixels identified.
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Are there any free hyperspectral image datasets available of image compression?
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Yes, Image compression are methods to reduce the size of images, there aren't specific data for image compression. you can test your own methods on such data.
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Hello all. I have RGB and infrared images taken with a drone. These images are in digital number (DN). Does anyone has a suggestion on how to convert the spectral bands to reflectance for calculation of vegetation indices? No reflectance panel was used onsite for any measurements.
Thanks!
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I would like to add a couple of broad comments:
1. Keep in mind that 'DN' stands for 'Digital Number' and has nothing to do with an image being calibrated or not calibrated or raw spectral band or ratio or PCA results or anything else.  EVERY image assigns a DN value to the pixels.  What is important is what the DN values represent !  If it is raw Landsat TM or drone image data it is the radiance received at the sensor and adjusted / converted to a digital number range based on the specifications of the sensor typically using gains and offset (or bais) values; if an image has radiometric calibration procedures applied then it can represent TOA radiance, TOA reflectance, surface reflectance, or what ever (even for DEM images the pixel values are DNs that in this case represent elevation).  Again, every image (no matter what they represent) has DN values and I would suggest that 'DN' not be used to imply raw image data only as it is being used in this conversion.
2. Since the early days of Landsat MSS 'broad' spectral bands have been used for digital remote sensing analyses and mapping, so I do not agree that your drone aerial images can only be used for photo interpretation.  I worked with some of the very first Landsat MSS images in early 1972 and have since then used many 'broad' band spectral image data sets for various application.
Pat
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After creation of the different VIs, what is the principle (or on what criteria and basis) does the forest health tool classify the different VIs into a single classified image?
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The ENVI family of image analysis software offers a full suite of tools to extract all types of vegetation-related information from any type of imagery. And, ENVI can be easily customized to solve your unique challenges, making it a highly flexible solution for agricultural and forestry applications. ENVI easily integrates into GIS workflows, allowing you to quickly and accurately view, manipulate, process, and analyze imagery. Because ENVI products are tightly integrated with ArcGIS® , you can easily exchange data and layer files between the software packages, saving you time and effort.PDF enclosed 
Assesment of ENVI Forest Health Tool in Detection of Dust and Seepage Contaminated Forest Areas
Abstract : ENVI software package is widely used in hyperspectral data analysis. ENVI features a forest health tool, which creates a spatial map showing the overall health and vigor of a forested region. General and quantitative forest health index, capable of detecting different types of forest damages, would be very useful in forest monitoring. The main objective of this study was to assess forest health tool's capability to detect dust and seepage contaminated forest areas. These areas were classified using various combinations of vegetation indices (VIs). The combination of Normalized Difference Vegetation Index, Carotenoid Reflectance Index 1 and Water Band Index produced the best classification results. Overall results of this study shows that ENVI forest health tool can provide valuable information when detecting dust and seepage contaminated forest areas.book ISBN :978-1-4244-2807-6
book e-ISBN :  978-1-4244-2808-3,DOI10.1109/IGARSS.2008.4779612
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I want to generate the test & train data samples from the hyperspectral image of indian pines & pavia university.
I also want to know a single pixel have multiple values due to bands. Which value is taking for training & testing process.
I am using Matlab as a simulation tool.
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A different sampling method.
Pixels selected for training, classification model applied on all the images and 50 pixels per image were randomly selected as ground truth.
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Hi everyone, what would be a good image fusion method for multispectral images, such as NIR (near infrared) and SWIR (short wave infrared) images? Or in general across the light spectrum? I would really appreciate if you can give some insights and provide/share the codes. Thanks.
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I will be thankful somebody that can attached my needed file hear or in my email adress:
Or Please guide me,  how can I request some free space born hyperspectral data of my needed area with attached coordinate ?
I attached KMZ  Google earth file of my needed coordinate area.
Thanks every body.
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or contact ESA for CHRIS Proba-multi-angle data
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I have 3 D dataset of indian pines. It has 16 features. How to implement PCA in this. 
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Syntax
[COEFF,SCORE] = princomp(X)
[COEFF,SCORE,latent] = princomp(X)
[COEFF,SCORE,latent,tsquare] = princomp(X)
[...] = princomp(X,'econ')
 
 
Description
COEFF = princomp(X) performs principal components analysis (PCA) on the n-by-p data matrix X, and returns the principal component coefficients, also known as loadings. Rows of X correspond to observations, columns to variables. COEFF is a p-by-p matrix, each column containing coefficients for one principal component. The columns are in order of decreasing component variance.
princomp centers X by subtracting off column means, but does not rescale the columns of X. To perform principal components analysis with standardized variables, that is, based on correlations, use princomp(zscore(X)). To perform principal components analysis directly on a covariance or correlation matrix, use pcacov.
[COEFF,SCORE] = princomp(X) returns SCORE, the principal component scores; that is, the representation of X in the principal component space. Rows of SCORE correspond to observations, columns to components.
[COEFF,SCORE,latent] = princomp(X) returns latent, a vector containing the eigenvalues of the covariance matrix of X.
[COEFF,SCORE,latent,tsquare] = princomp(X) returns tsquare, which contains Hotelling's T2 statistic for each data point.
The scores are the data formed by transforming the original data into the space of the principal components. The values of the vector latent are the variance of the columns of SCORE. Hotelling's T2 is a measure of the multivariate distance of each observation from the center of the data set.
When n <= p, SCORE(:,n:p) and latent(n:p) are necessarily zero, and the columns of COEFF(:,n:p) define directions that are orthogonal to X.
[...] = princomp(X,'econ') returns only the elements of latent that are not necessarily zero, and the corresponding columns of COEFF and SCORE, that is, when n <= p, only the first n-1. This can be significantly faster when p is much larger than n.
 
Examples
Compute principal components for the ingredients data in the Hald data set, and the variance accounted for by each component.
load hald;
[pc,score,latent,tsquare] = princomp(ingredients);
pc,latent
pc =
-0.0678 -0.6460 0.5673 0.5062
-0.6785 -0.0200 -0.5440 0.4933
0.0290 0.7553 0.4036 0.5156
0.7309 -0.1085 -0.4684 0.4844
latent =
517.7969
67.4964
12.4054
0.2372
The following command and plot show that two components account for 98% of the variance:
cumsum(latent)./sum(latent)
ans =
0.86597
0.97886
0.9996
1
biplot(pc(:,1:2),'Scores',score(:,1:2),'VarLabels',...
{'X1' 'X2' 'X3' 'X4'})
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I want to know the maximum possible value at each of the 220 spectral bands of AVIRIS? is there a way to know it? FWHM is available but i dont understand what is it??
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What is the source and the current level (L1 or L2 or L3) of the AVIRIS data that you are using? Do you have any other information in the metadata regarding the units? Is it in raw DN values or Radiance or Reflectance? Depending on the level of the data the units would differ and therefore the min-max data value range. For example, the Surface Reflectance product typically has data values ranging from 0 to 1 (unitless). Attached links might be helpful.
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I want to calculate Photosynthetically Active Radiation (PAR) from band1 values of MOD02QKM to be used in satellite model.
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Thanks Nasem and Abdegdir for the reply. MRT process works fine with MOD09 and MOD13. However it couldn't process MOD02 which is a Level 2 product that lacks sinusoidal coordinates (V##h##). I just did  research and found out that the recommended software to process MOD02 is MRTSwath. I want to know the steps to derived Band 1 for daily measurement using MRTSwath Tool. I want to get final values in text file or other format as long as it appears in numbers for calculation purpose.
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Hi, can anyone give the clarification of groundtruth image or dataset. Actually what it is and how the ground truth database has been creating. 
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The observed color at any point on an object is influenced by several factors like  the shape and material of the object, the positions and colors of the light sources, and the position of the viewer.
Ground truth is a common terminology that is widely used in various fields to basically refer to any kind of information provided by direct observation.
In Remote Sensing, Ground truth is the information or data collected on site so that the input data (image) can be related to the actual features and is considered to be much correct and appropriate than the input features. This process compares the pixel on a satellite image to what is there in reality (at the present time) in order to verify the contents of the pixel on the image. Further, Error of Commission and Omission can be calculated for accuracy assessment.
Kindly refer to the paper attached where the researchers has tried creating the ground truth dataset for image algorithms (Ground Truth1.pdf) and the second pdf is a chapter on the same.
Hope it will help u in some way.
Regards,
Rebika Rai
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I want to download only the panchromatic band of Landsat 8 for ~10 images instead of downloading the whole 10 images (each ~1 GB )
Thanks in advance 
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Hi Ayman Hassan 
You can also try The Semi-Automatic Classification Plugin (SCP) for QGIS made by Luca Congedo. This is the link where you can have an overview of it: 
It's very nice to use and can help you  to download individual band of Landsat 8/7/4-5/1-5 scenes separately.
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For quantitative purposes, hyperspectral imaging seems to lead to a bigger prediction error when comparing its prediction ability to traditional NIR instruments. Despite its advantages and potential, this seems to me a relevant issue for a full implementation of this technology. 
Which factors are in your opinion the most important ones in leading to lower performance? There might be the effect of sample position, illumination condition, the difference which is obtained when looking at single pixel level or averaging areas (especially when applying the calibrations!), the fact that the sample is moving (in a push-broom system, for example), and further error is obtained when looking at single objects instead of average batches.
Thus, are there actual benefits of using HSI over traditional instrumentation to study the composition of food and agricultural products? 
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There are many factors affecting the accuracy of VNIR hyperspectral imaging. 
First is your samples and the way you do the reference analysis. Let's suppose you are working on kernels to measure for example N content. How many kernels do you use for measuring N content using your ref method? If 5 kernels, then you should use the average spectra of all the 5 kernels to correlate to the reference N (There are more approaches but I think this is the best for linear models). 
Second, your camera properties. I mean resolution and spectral range of your camera? Smaller resolution and wider range are more favourable.
Third, outlier detection and data pre-processing are so important. Dimension reduction and/ band selection also improve the accuracy of the models. 
Fourth, the model you use for calibration. Is that a linear model or a non-linear? Most of the times linear models fit well to your data but sometimes you need to use nonlinear models.  
The number of the samples you are using for calibration is also important more specifically when you are using machine learning models such as ANN.
Once you develop the model, then you can use any pixel for prediction in new samples. Even you can use this system Real time. 
Hope I could add anything to what you already know. 
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I want to know how to differentiate the spectral profile of Tea from other Land uses. Is reflectance of tea leaves have any distinct qualities in NIR region than other vegetation, forest , soil and water?  
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I have the ground truth map of indian pine and the pavia university images, but this ground truth is for classification not for segmentation
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Hi Tabia,
As an alternative in a segmentation context, I suggest you look at this paper: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2013/Jordan13-MCF.pdf
Here is a excerpt from this paper: "We test our algorithms on the CAVE multispectral image database [8]...  Ground-truth data for evaluation of a global clustering is not available and would be very difficult to obtain. Therefore we mostly rely on qualitative evaluation of the segmentation results."
Hoping it will be helpful to you.
Regards
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the current cloud removal algorithms are almost aim to the multispectral or pan remote sensing image,i want to know that does these methods suitable for hyperspectral image.
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If you want to generate method  or algorithm for cloud removal, you may learn about atmospheric window and its interaction with electromagnetic wave. Then, check and learn about EM range of the hyperspectral satelit sensor that used.
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I am simulating multispectral sensors to acquire multispectral images from hyperspectral data. What type of noise should be modeled during the acquisition? There is already some noise in the hyperspectral data so only Gaussian noise may not be sufficient for such a model? 
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Hi Haris,
Another reference to corroborate the diversity of noises to take into account when simulating a multispectral / hyperspectral sensor :
The accuracy of the model will depend on your needs. If your interest focuses on optical lenses, here is a good reference:
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I am undertaking a project into the identification of tree species using satellite data, namely the hyper spectral data generated by Sentinel 2. Is there a way to use this data to identify trees to a species level or failing that identify whether a tree is broadleaved/coniferous etc?
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Although tree species level identification has always been challenging if your are working at 10 m spatial resolution.
1. So Before going for individual tree species level analysis using Sentinel-2. You must go for some preliminary analysis using Sentinel-2 data. Sentinel -2 provides 10 m spatial resolution for RGB and NIR bands while it provides 20 m spatial resolution for band 5, 6, 7 and 8A in Vegetation RedEdge category. 
2. Since RedEdge bands has capability to discriminate different tree species based upon their spectral variability. Therefore, Sentinel-2 can be a powerful opensource platform for these kind of analysis. 
3. If you do re-sampling of RedEdge bands to 10 m and create the Layer Stack of Red, Green, Blue, NIR and RedEdge Bands and then you can check the spectral signature of different trees species (it could be some homogeneous regions) for different band combinations. 
4. For this you must have some ground data. If you have a then you can cross check these spectral signatures and then you can go ahead for either object based or pixel based supervised classification procedures. 
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I want to create a realistic instance with the help of STK (Satellite Tool Kit) Consisting of 6 identical satellites moving on the same orbit (circular, altitude 668 K.M, and sun-synchronous) with a phase shift of 120 degrees between each satellite. The provided instance is artificially generated, with 800 tasks (target for imaging), 1500 strips, and 912 opportunities. (these numbers just for instance).
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Dear Mohamed Atef Mosa,
I suggest to you a links and attached files in topics.
- STK - Analytical Graphics, Inc.
-The Nog » Satellite Tool Kit - AGI Blog
-From lynn.wheeler at firstdata.com Tue Jan 1 12:59:28 2002 From ...
Best regards
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Remote sensing, Application of remote sensing in water bodies
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In near IR the reflection of clear water  is low compared to other land surface features. However in turbid water it may show a higher value.
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Hi all
I am using the Cytospec software for FCS analysis and I am stuck at the most simple stuff...how do I make an overlay on the histogram?
Thanks!
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why do'tn u use matlab or python?
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Hello every one, in my  current research, i am working with AVIRIS data for rock mapping and also mineral prospectivity determination by mineral potential zones mapping. In this case there are lot of issues  regarding data processing and interpretations, I have applied MNF, CEM SID but i did not make a good result in output. so in this regard if anyone can help me , i will be highly obliged...thank you
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Hi everyone,
I have a query and a request; query about how to make  effective per-processing steps in AVIRIS (Hyper- spectral) data processing? I have done some steps like empirical calibration, MNF and some more. But i could not remove noises from radiance data. So in this situation, my kind request to you all if anybody has experience, knowledge or expertise, please  en-light me with yours wisdom.
Add on: If you can please attach some link or literature of good publications related to pre-processing of AVIRIS data.
Thank you 
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Hi,
if you consider not geo-corrected radiance data we can give this approach http://dx.doi.org/10.3390/rs61111082 (most pushbroom sensors are affected by temporal variations of the dark current and related miscalibration) or this approach http://dx.doi.org/10.3390/s110606370 a try else there are some smoothing techniques as Atilio said.
Cheers, Chris
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In hyperspectral analysis, once pure pixel locations are fouund out, how to compare it with the target spectra?
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Thank you so much Mam. I will read the paper which you have shared.
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Since iron oxides are one of the best key indices for mineral exploration. In addition integrating spectral imagery data can provided effective discrimination and mapping of exposed rocks. So I decided to design and apply a UAV malutispectral imagery system. For this reason, I want to know, is it correct applying Parrot SEQUOIA multispectral camera on drone?
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Hi Nasser
I don't know of any studies that demonstrate the use of multispectral imagery from UAVs to map iron oxides, in contrast to satellite studies... The extreme difference in spatial extent and resolution between satellite and UAV data might mean that satellite methodologies for detecting iron oxides might not work for UAV data, even with the same band combinations, due to small scale noise and other confounding factors in the UAV imagery...
That said, the iron indices that I know of make use of a narrow blue band, which the Parrot Sequoia lacks, but is available on e.g. the 5-band MicaSense RedEdge multispectral camera for UAVs (and other 4-band cameras that allow for custom band placement as opposed to the Sequoia).
Based on Landsat TM data, the Iron Oxide Ratio = Red / Blue was developed, both of which correspond reasonably well with the MicaSense RedEdge red and blue bands. Based on WorldView-2, the WV New Iron Index = (Green * Yellow) / (Blue * 1000) was developed, for which you need again the narrow blue band, while the yellow could be conceived as a (properly weighted) average of the green and red bands when using the MicaSense RedEdge camera. Last, there's the Iron Feature Depth (IFD), measured as the depth of the 900 nm absorption peak of secondary iron bearing minerals, from the line between the 750 and 1250 nm shoulders. However, I don't think that either the Sequoia or the RedEdge cameras would be suited for this as it requires a SWIR band, and both cameras have red edge and NIR bands placed somewhat too low for the other features in the IFD calculation (the Sequoia even much worse than the MicaSense RedEdge in this respect).
Hope this helps
Klaas 
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Vegetation index as i know is used for estimation with the help of field data, but how are they used in spectral enhancement preprocessing of Spectral image?
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I'll second what Atiqa Khan says. Vegetation indices are band ratios which by their very nature enhance the information content of imagery.  Just looking at an NDVI image you already know which areas are likely vegetated. That's what is meant by spectral enhancement. Because of this  enhancement aspect, you can classify a stand alone NDVI image by determining thresholds or you can add it as an extra input band when classifying multispectral. 
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Whether hyperspectral sensor penetrate the surface of earth or it captures the surface level material?
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Hyperspectral data have been used to detect sub-soil features in various disciplines. The MIVIS sensor was used in Archaeology:
Traviglia, A. (2006). MIVIS Hyperspectral sensor for detection of subsoil archaeological sites and interpretation support GIS: an Italian case study. Digital Discovery. Exploring New Frontiers in Human Heritage, 18-26.
ARPA has a project going on mine detection using hyperspectral data:
DePersia, A. T., Bowman, A. P., Lucey, P. G., & Winter, E. M. (1995, June). Phenomenology considerations for hyperspectral mine detection. In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics (pp. 159-167). International Society for Optics and Photonics.
Hyperspectral data was part of the input to this study on topsoil mapping:
Selige, T., Böhner, J., & Schmidhalter, U. (2006). High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma, 136(1), 235-244.
In this article are discussed methods to study and monitor soils with hyperspectral data:
Viscarra Rossel, R. A., Taylor, H. J., & McBratney, A. B. (2007). Multivariate calibration of hyperspectral γ‐ray energy spectra for proximal soil sensing. European Journal of Soil Science, 58(1), 343-353.
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What is Extended Morphological Profiles in hyperspectral imaging. I need to apply in pavia dataset (Mat file). Detail of the file is present in the below link.
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Did you solve your problem meanwhile? Extended Morphological Profiles in Hyperspectral imaging means that the datasets available are more precise because of the integration with other datasets. For coding application check Github or this book might be helpful, although is a little outdated http://isp.uv.es/papers/rem_sens_im_proc_12_ch02.pdf
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It is known that FLAASH and QUAC atmospheric correction not suitable for thermal bands. So what is an ideal method of atmospheric correction of Landsat 8 thermal bands (TIRS)?
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Hi,
The following web link might be helpful for you.
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I have downloaded level 1Gst and level 1T hyperion hyperspectral data for my study area, in order to use in mineral mapping, however, every time I try use FLAASH for atmospheric correction it gives an errors or bad results. Should I use the toolkit then subset (removeing bad bands) then FLAASH or not? Also should I put specific advanced paramters in FLAASH?
How I use FLAASH atmospheric correction using ENVI (what ideal parameters for such data sets)?
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hai first remove bad bands and water vapour 1-7 58-76 165-182 221-242 you have 163 bands around use radiometric calibration tool convert DN to radiance. after that use FLAASH or QUAC my friend.scale factor 0.1 for radiance conversion
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 is it possible to generate the signature of any tree species without spectometer
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In any case contiguous signatures, only can be derived from spectroradiometer handheld/satellite based. If you have handheld spectroradiometer data it is nice, but if you don't have, you can generate signature for a pure patch of single species of size about/more than 30*30 meters using Hyperion based hyperspectral data freely available on earth explorer. The patch size may vary depending upon spatial resolution of the hyperspectral data (space borne/ air borne).