how to extract features from a satellite image will all feature matrices having same size?
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i want to classify a satellite image based on its NDVI values. i have to classify the image in three classes: wheat, fodder and others. when i extract wheat (ndvi>0.5), fodder(ndvi<=0.5 and >0.3) and others(ndvi<=0.3) pixels from image, the three column matrices have different sizes due to which i cant apply patternnet to it. plz help me by telling some efficient way of feature extraction. plz also tell me if it is possible to train the neural network first for a sample of the image and then use it to classify the whole image? i would really b obliged if any one answer. badly need a quick response
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Accepted Answer
Greg Heath
on 30 Mar 2014
Since each pixel has a different classification, the rxc image is columninzed and transposed to an Nx1 row.
input = image(:)';
The corresponding Nx3 target matrix contains 3-dimensional columns fro the 3-dimensional unit matrix eye(3).
Hope this helps.
Thank you for formally accepting my answer
Greg
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Greg Heath
on 30 Mar 2014
sizes already given
help ind2vec
help vec2ind
For examples search using
greg ind2vec vec2ind
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ashish bha
on 12 Aug 2015
respected sir , i am working on satellite image classification using BBO with CS algorithm with Urban , vegetation, barren , water , rocky , can you pl help me image classification of its satellite image if you can pl : ashish88bhardwaj@gmail.com Thanking you.
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