Question

Asked 27th Feb, 2020

University of Leipzig

How to calculate Multivariate Image Texture in Google earth engine or any other software?

Dear all,

I am trying to classify urban areas using Landsat TM data, I have tried to get the image texture using GLCM and the result is somehow good, but I have seen many other literatures which exploited Multivariate Image texture and had better result compare to GLCM method.

GLCM: uses one band as input data

Multivariate Img text: uses multi-band as input data

here is a paper regarding Multivariate Img Text : https://pdfs.semanticscholar.org/e5b4/9480f331eaf33de24f48b2304060438f2333.pdf

I appreciate your help.

regards,

Salim

Texture

Images

9th Nov, 2021

Salim Soltani

University of Leipzig

Hi Yashon, I couldn't find anything :). Good luck!

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Parisa Ziarati

Formerly Academic member in Pharmaceutical Faculty

Anoop Kumar Srivastava

ICAR-Central Citrus Research Institute ( Formerly National Research Centre for Citrus)

J. C. Tarafdar

Central Arid Zone Research Institute (CAZRI)

Srinivas Kasulla

Arka Brenstech Private Limited

Aref Wazwaz

Dhofar University

2nd Nov, 2020

Alireza Taheri Dehkordi

Khaje Nasir Toosi University of Technology

Use the below link for GEE:

https://developers.google.com/earth-engine/guides/image_texture

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1 Recommendation

2nd Nov, 2020

Salim Soltani

University of Leipzig

Thank you Alireza, I am looking for Multivariate texture, not GLCM. It is not available in the google earth engine.

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8th Nov, 2021

Yashon Ouma

Moi University

Hi Salim, looking for the same. Did you succeed?

Cite

9th Nov, 2021

Salim Soltani

University of Leipzig

Hi Yashon, I couldn't find anything :). Good luck!

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How to calculate and plot the feature importance of the input dataset of a random forest classification within Google Earth Engine (GEE)?

Question

9 answers

Asked 9th Oct, 2020

Marcel Mohr

I run a random forest classification for agricultural land use and other land cover classes (12 classes). My dataset for 2019 consists of a Sentinel 1 and Sentinel 2 monthly time series, statistical phenometrics (e.g. min/ max/ median/ stdDev) and ancillary data as texture metrics and topographic derivates. I included a lot of different indices calculated on the basis of the sentinel 2 optic bands. This resulted in a single image with 294 bands as a big input data cube for the random forest algorithm. In order to decrease computational time I would like to calculate the feature importance of the different bands in that image for each one class against all classification and then only use the most important bands. So far I didn't find a lot in different blogs and platforms.

I found an approach on MyGeoBlog that is unfortunately showing an error that I couldn't resolve. In theory this approach should work with the one class against all other classification approach. Find my adjusted script with the following link (note that its a different dataset with only 216 bands without the sentinel 1 data):

https://code.earthengine.google.com/?scriptPath=users%2Fmarcellitomohr93%2FArmenia_LULCC%3ALULC_Classification2019%2FGoris2019_ClassificationIterations%2FFeatureImportance_GrasslandMask

The original script on MyGeoBlog you may access via this link (however it is lacking input data):

https://mygeoblog.com/2020/02/12/random-forest-trees-and-variable-importance/

In my case GEE shows the usual point feature size exceeding error but also the error: "Error generating chart: Property 'Grass_Mask' of feature '0' is missing." Since for this script you can only calculate probability with 2 classes I transformed my 12 classes of a LULC_Class property to a binary property column that is called grass mask (with only values of 1 for grassland and 2 for all other classes).

Im happy to hear about your thoughts and experiences with calculating the feature importance in the GEE environment. I could also do it in R but then I have to download my very large dataset.

With best regards,

Marcel

Code of GEE for monthly NDVI using Landsat 8 Data in Google Earth Engine ???

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10 answers

Asked 29th Jan, 2020

Ahmadi Mehamoui

Hello Everyone

I want to calculate the NDVI values for each month using Lnadsat 8 data then showing as the trend on the chart on google earth engine. I tried to write the code for making monthly NDVI using landsat 8 data but in this code error is still coming.Please help me to sort out it this error.

Thank you in advance

What is Difference between the top of atmosphere radiance and surface radiance and their ranges?

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27 answers

Asked 20th Jan, 2017

Abbas Einali

hello

converting Landsat TM5 digital number to Reflectance is my aim. I have studied that reflectance range is between 0 and 1 and it is dimensionless. I downloaded a Landsat TM5 surface reflectance of USGS website and opened in ENVI. Pixel values (reflectance) of the image didn't confine between 0 and 1 and were about 1000. please help me. The surface reflectance image can be right?

what is a difference between the top of atmosphere radiance and surface radiance?

Thank you.

Out of User's and Producer's accuracy, which of these two is relatively important?

Question

8 answers

Asked 31st Jan, 2020

Vineela Nandam

To assess the accuracy of LULC classified images, a confusion matrix is created and overall accuracy, Kappa coefficient and user's and producer's accuracy are to be calculated. In a confusion matrix, which among user's and producer's accuracy is significant? Also, if we multiply user's and producer's accuracy (for eg. user's accuracy is 80% and the producer's accuracy is 78%, on multiplying 80% and 78% we get 62.4%), can we draw any discussion on the classified image?

Atmospheric correction code in Google Earth Engine (GEE)?

Question

23 answers

Asked 27th Oct, 2020

Elias Berra

I am looking for options of GEE JavaScript code to perform atmospheric correction of Landsat 7-8 and Sentinel-2 directly within the GEE Code Editor.

I intend to use harmonized Landsat 7-8 and Sentinel-2 bottom of atmosphere (BOA) reflectance data across some locations in Brazil during 2017-2018. While Landsat 7-8 BOA data are readily available in GEE (at least for my region and date), Sentinel-2 BOA data are not always available in GEE (yet), depending on the region and date. Therefore, I may need to apply my own atmospheric correction on the Sentinel-2 TOA data - preferably within the GEE Code Editor in order to perform as many processes as possible inside GEE.

To improve harmonization among the different satellite data, the same atmospheric correction method could be applied to Landsat 7-8 TOA data as well.

In summary, it would be great to apply the same atmospheric correction method on Landsat 7-8 and Sentinel-2 data directly within the GEE Code Editor.

How could I process Level-1C imagery from Sentinel-2 in Google Earth Engine with Sen2Cor?

Question

5 answers

Asked 7th Dec, 2021

Bruno Rech

I'm about to start some analyses of vegetation indexes using Sentinel-2 imagery through Google Earth Engine. The analyses are going to comprise a series of images from 2015/2016 until now, and some of the data won't be available in Level-2A of processing (Bottom-of-Atmosphere reflectance).

I know there are some algorithms to estimate BOA reflectance. However, I don't know how good these estimates are, and the products generated by Sen2Cor look more reliable to me. I've already applied Sen2Cor through SNAP, but now I need to do it in a batch of images. Until now, I couldn't find any useful information about how to do it in GEE (I'm using the Python API).

I'm a beginner, so all tips are going to be quite useful. Is it worth applying Sen2Cor or the other algorithms provide good estimates?

Thanks in advance!

How to Compute Jeffries matusita distance on GEE?

Question

Be the first to answer

Asked 11th May, 2021

Siyamthanda Gxokwe

Can anyone share the Google earth engine script to computing Jeffries matusita distance for assessing class separability

Code of GEE for phenology metrics using monthly NDVI or EVI Landsat Data?

Question

25 answers

Asked 1st Apr, 2020

Eduarda Silveira

Steps that I need:

1. EVI L8 time series reduced by montly median values;

2. Fit a curve using Savitsky-Golay, Whitakker or Harmonic model;

3 - Extract phenometrics, for example, SOS (start of the growing season) and EOS (end of the growing season).

Do someone knows how to perform these steps using Google Earh Engine?

Thanks

How to stack layers in Google Earth Engine (GEE)?

Question

8 answers

Asked 8th Jan, 2019

Nur Shafira Nisa Shaharum

I want to perform image classification in GEE, i want to select only 7 bands from landsat data and stack it with ndvi.. How to perform that?

Image Texture

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Dec 1999

The sections in this article are

Shape from Monocular Images

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It is in principle impossible to capture three-dimensional information from a monocular image. However, if something is known about the scene, a monocular image or multiple monocular images (viewed from the same position) may often provide information on three-dimensional surface orientations, though not complete range information. The shape of obj...

Detection of Near-Duplicate Images using Statistical Texture Features

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