Questions related to Hyperspectral Remote Sensing
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.
CI-110 Plant Canopy Imager gives two reading related to leaf area index (LAI) GF lai and PAR lai. Is these values are the final ones if I am trying to Effective leaf area index or do I need to do any other calculation ? if yes, then, Which values should I use as the effective leaf area index (GF lai / Par lai)? I am trying to work on lai prediction using remote sensing.
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.
Are there books & papers dealing with methods to detect active fault by using hyperspectral remote sensing. Or does anyone have good idea?
The availability of low price hyperspectral images that can cover large area with high spatial resolution has not matured yet. After the termination of the Hyperion mission on EO-1 satellite and the same for Hyperspectral Imager for the Coastal Ocean (HICO) the choices of hyperspectral satellites are decreasing. Do you think that this technology is going to extinct and give up to aerial hyperspectral technology or there is still hope?
Full-time, benefited position with the Department of Viticulture & Enology/Viticulture and Enology Research Center through the California State University, Fresno Foundation. The Department of Viticulture & Enology/Viticulture and Enology Research Center is seeking a PostDoctoral Scholar who is experienced in computer vision or remote sensing and is passionate about Machine learning/Deep learning, automated and digital agriculture, and chemometrics. We are interested in working with a motivated scholar who can think creatively and practically about production-oriented remote sensing and “big data” applications in agriculture. Strong quantitative background is preferred (as demonstrated by publications), experience with unmanned aerial vehicle and hands-on electronic ability is a plus. This position is based within the Department of Viticulture & Enology/Viticulture and Enology Research Center, through the California State University, Fresno Foundation. The selected candidate will also have the opportunity to collaborate nationwide with scholars from other universities (Cornell, UC ANR) within multi-institution projects funded from USDA, CDFA and CSU-ARI. Within overarching project goals, the candidate will have intellectual freedom to develop and pursue the research directions they find most interesting. Mentorship will focus on helping the candidate meet their professional development goals, whether in academia or elsewhere. The position is 100% time (40 hours per week), benefited, and funded for 12 months initially, may be renewed based on funding and contingent on satisfactory progress.
PLEASE FIND THE FULL CALL AT THIS LINK:
In microwave radiometry of soil moisture, what is the typical magnitude or range of errors introduced by variations in soil roughness and vegetation cover? Soil texture can also affect accuracy, but soil roughness and vegetation cover seem to be most important.
I am looking for getting hyperspectral remotely sensed data from any satellite which covers Egyptian lands. please if any one knows how to get those images.
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
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?
Spaceborne Hyperspectral observation (i.e. hyperspectral remote sensing in uv-visible-infrared spectral range) of Earth and for Planetary science, plays a very important role in improving scientific understanding, environmental and resource monitoring.
Signal to Noise ratio (SNR) is a very important parameter (or quality metric) of any Hyperspectral instrument indicating its potential to meets its desired observational goals.
Due to demanding need on higher spectral and spatial resolutions, it become challenging to good / high SNR to meet the desired observational goals.
In view of this I wish to discuss or seek suggestions of various options or ideas by which SNR of Hyperspectral instrument can be improved. Ideas or options may be either for instrument design aspects or for image or data processing aspects.
Models that evaluate parameters to water quality. For example clorofila a, turbidity, DO, etc.
Models for lakes in the tropical zone.
I want to extract RCS values or backscattering coefficients from available Radar imagery and create a Geospatial layer out of it.
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.
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!
Where can i find open datasets regarding " Wheat/Maize Leaf Spectra with Associated water content, Potassium and Nitrogen Measurements" for machine learning research i.e. hyperspectral tabular data collected from a field spectroradiometer along with chemical parameters.
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?
I am looking into the feasibility of attaching a Specim FX17 sensor to a multi rotor system.
Specs wise it looks possible, sensor is 1.4kg and Matrice M600 max payload is approx 15kg.
Does anyone have any experience?
If anyone knows, please tell me where I can get Village level administrative boundary (Level 4) of India in ESRI shapefile format?
Is there any standard procedure/sequence of tools to process the hyperspectral tabular data before PLSR regression modeling.
example of tools are 1) De-resolve 2) second derivative 3)normalize 4) de-trending 5)baseline etc.
Application is for field spectroradiometer data of soil and crop.
Or the sequence of tools differ for different datasets ?
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.
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?
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?
I'm a student who is starting in the remote sensing field, specially in ocean and coastal studies.
Can someone explain me how to get the Remote Sensing Reflectance (Rrs) from Sentinel 2 images?
Until now I was using the pixel value as Rrs, but looking at
Thanks in advance
Hello everyone. I am working on sustainable infrastructures and my goal is to select the most optimum site for infrastructures in my area of interest to gain maximum sustainability. To achieve my goal i need to know whether the area of interest is contaminated by heavy metals, but for some reasons my priority is to use remote sensing to know the fact. Unfortunately remote sensing is not my expertise, so this is why i asked this question. Actually i need a clear and simple answer if it is possible.
I am trying to orthorectify an IRS 1C and an IRS 1D multi - spectral image using a DTM/ DEM. I have Cartosat 1 DEM of 1 Arc - Sec (30m). There are no RPCs provided by the vendor and I was told that the product isn't orthorectified nor geometrically corrected.
I use Erdas Imagine 2015 for working with Satellite Data. Any help on this will be highly appreciated.
I am evaluating the potentials of using S2 imagery with spectral unmixing techniques to retrieve physical properties of beach sand, such as granulometry, humidity and possibly spectrafacies (intendend as discrete classes of mineral mixtures occurring along the Victorian coastline, Australia).
The endmembers I will use are processed based on my VNIR (350-1075 nm) field spectroscopy campaign (ongoing) and planned Vis-SWIR (350-2500 nm) lab-based spectroscopy of sand samples from cross-shore profiles.
I think I will use Linear Spectral Unmixing, depending of the level of intimate mix I will discover in my sand samples.
However, I find it hard to find good literature.
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.
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..
I am trying to process the wheat crop data collected using ASD for reflectance. However, I see two different options in ViewSpecPro software- Absolute reflectance and reflectance(transmittance). May I know the difference between them and what should be appropriate in studying variation in leaf reflectance among wheat genotypes due to disease and stress?
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.
I am currently trying to focus ALOS Level 1.0 data using Sarscape 5.1 in order to do SBAS-InSAR work.
I have no experience in doing so. I am not happy with the result I obtain, please see an example of result here (over urban area)
The settings I use:
Data type: ALOS PALSAR Single Pol
Global and Other Parameters are set to default
Can you confirm the result I obtain are not what I am suppose to obtain ?
How can I improve the focusing ?
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.
I am gathering papers about intertidal sediment (sandy beaches) characterisation (facies mapping) based on spectroradiometry.
I am planning to go out in the field, collect sand samples from the swash zone to the backdune depositional environments, evaluate sediment charachteristics and relate them to their spectras.
The spectral profile of bare sand is almost featureless within the VIS range, however, by using a 300-1000 nm spectroradiometer, I hope I will get enough heterogeneity in spectral response to map different "sands" within a sandy beach.
The literature is full of papers about using spectra to get sediment granulometry, mineralogy, organic matter, humidity, and other variables.
However, I am looking into papers about sand classification with broader classes that intrinsically inherit the complex spectral response from their biophysical heterogeneity .
* 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?
External Parameter Orthogonalisation (EPO) algorithm is used to remove the effect of soil moisture from NIR spectra for the calibration of SOC (Soil Organic Carbon) content. This algorithm is used for pre-processing of spectra of soil taken from spectroradiometer and then PlS (Partial Least Square) regression is apllied.
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 !
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.
While spatial, spectral and temporal advantages of multitemporal hyperspectral images provide opportunities for advancing the compression and classification techniques. So I need multitemporal remote sensing hyperspectral data 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 hyperspectral images then please give the link here.
I am interested to work on the archaeological mound. So for the preparation of the digital map of the archaeological mound which software can be used?
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
For review study, I need the name of latest existing techniques used for remote sensing satellite image compression for lossless, near lossless, lossy in all possible cases such as predictive based coding, transform based coding, dictionary based coding. hybrids coding etc.
Need your help or suggestion plz reply.
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.
is there some algorithm or technique to detect specific vegetation species using hyperpsectral remote sensing on the basis of there spectral signature?
Geothermal Energy has long been regarded as Green Energy's silver bullet. And rightly so because of its potential as a stable, secure, environmentally friendly and a cheap Energy resource. But there are some obstacles.
Firstly, Not everyone lives on top of a Volcano! Unlike wind and solar installations which can be built more or less anywhere, Geothermal Power Plants can only be built on geological hotspots that exists less than 10 % of our planet. Secondly, even in hot spots like Iceland, Engineers must deal with the most daunting technical challenge facing geothermal. Drilling! That's because all successful Geothermal ventures require three vital elements. Hot Rock, Water and close proximity to the resource. Thirdly, money and potential exploration risks are additional issues to be dealt with.
Remote Sensing techniques have been aiding in identification of Geothermal Hot spots with thermal anomaly, geological and mineral mapping studies. Geophysical techniques such as resistivity, gravity, seismic and geodetic measurements are valuable in the exploration phase. Next, Geochemical sampling and analysis takes over. RS & GIS are also applicable in Environment Impact Assessments after a Geothermal Power Plant is set up. Recent developments have seen the arrival of Unmanned Aircraft Vehicles (UAVs) for geothermal exploration.
What are the challenges faced by the Geothermal Explorers in different locations such as Indonesia, Kenya, US, Philippines, Japan, Iceland, NZ, Aus., EU and elsewhere. Are there any potential hazards due to their proximity to Volcanic systems.
Requesting all subject-matter experts to enlighten with their relevant answers.
The Salinas scene hyperspectral data set can be found in the below link as a .mat file. The description says the values are in at-sensor radiance values. I need to convert them to surface reflectance for further analysis. Can anyone suggest a method and also mention the relevant sources used in the process.
I have high resolution aerial photography (12.5 cm) data which only has bands I, 2 and 3. I am trying to detect individual tree species.
How can I calculate a near infrared band for this data-set (the 12.5 cm resolution) using sentinel band 8 data (taken around the same date and time).
I am using Google Earth Engine to create a cloud free Mosaic for my study area.
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
While using the HySpex camera, I am facing the problem of Keystone distortion across the scan line. Although I am using the camera for close range imaging, this is a well-known problem in remote sensing community and therefore, I will appreciate if someone can help me to correct it. I have the sensor model file as well, but I am not sure how to use it for correction...
The fusion of panchromatic images, providing high spatial resolution and multi/hyperspectral images of lower spatial, but high spectral resolution, is well known under the term of pansharpening. In several talks, I've often noted the statement, that spectral overlapping of PAN and MS/HS sensors is a requirement to apply pansharpening approaches. I would like to ask whether this spectral overlap is a strict and general requirement or if a high correlation of spatial information between a PAN image and upscaled MS/HS band visualizations or components is sufficient? Does it depend on the choice of the algorithm (CS, MRA) ? Looking forward to your answers. Kind regards, Chris.
Could you please share your experience on the interpretation of red edge data in UAV remote sensing.
We recently used a drone with a camera with green, red, NIR, red edge and RGB spectrums.
I am confused about the meaning of red edge data: That is to say;
Is the data from the red edge spectrum/ band a kind of reflectance at a specific band? If so what is the wavelength range?
Or Is it the wavelength at which the reflectance curve sharply rises from red band to NIR band?
And also how to interpret the data?
Thanks in advance.
Muharrem Keskin, Mustafa Kemal University
I intend to detect the absorption features of heavy metals or the proxy minerals in the range of 400-2500nm and I plan to start with artificial contaminated samples. Shall I mix pure metal (e.g., Zn, Pb, Cu) with soils/minerals or mix the compound/solution (metal cation like Zn(II), Pb(II), Cu(II)) with soils/minerals? Any suggestions and literatures/links are welcome.
Thanks in advance.
I was performing the both classifiers for two different hyperspectral datasets. For one dataset SVM is performing better and for the other one RF is performing better. What could be the probable reasons for this results!
Doing research in HSI classification on SVM based classifier. I'm using standard data sets for classification. I want to validate the outcomes with other classifier. It's enough to compare with the existing works or need any specific? I'm confused in it. Advanced thanks for valuable comments.
I 'd like to do band selection on hyperspectral images, i Found a lot of techniques in literature. which one is the most significant? and is there any libraries that can help me?
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
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.
in an equation for emissivity calculation i should to determine the value which is exactly boundary between soil and vegetation and the value which is placed between mixture pixels(soil+vegetation) and full vegetation ones, on NDVI image using histogram.
I am looking for the most appropriate rockfall model from the most recent in-use rockfall models. The available information on rockfall hazard will be combined in a Geographic Information System with a map of the existing trail of Kinabalu National Park to identify the trail sections which are potentially subject to rockfalls and degrees of rockfall hazard.
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.