If you only have VV polarization classification is difficult. Typical supervised classification is based on samples which help to divide your feature space. But even if you have VV and VH, your feature space is only n=2 so your classes won’t be much distinctive. To increase your feature space you have the following options:
- add image textures (GLCM module), but this should be done before speckle filtering. Speckle filtering destroys most of the image texture.
- add images of different dates (dry and rainy season, for example)
- add images of a different sensor
- add topographic information (right-click > add elevation band)
As soon as you have a stack of bands with more than about 4 bands, supervised classification makes more sense. But if you are using inputs of different units (textures) you can no longer use the Maximum Likelihood or Minimum Distance classifiers. I’d recommend the Random Forest classifier.
I classified image based on SAR textures and additional bands for classifications and it worked out well:
If you have specific questions on one of the single steps, feel free to ask.
Related topics collected here.