Supervised classification for sentinel 1

Microwave Toolbox
5

Hi rehan123,

I know that in some cases (such as classifying trees species or agriculture areas) SAR data might not be the best option due to the fact the micorwave radiation is very sensitive to surface roughness and when interacts with objects such as trees and houses the backscatter signature is very similar and that leads to classification errors.

There are ways where you can improve the classification accuracy using SAR data. Some ideas are described below:

  • Use dual-polarization data such as SENTINEL-1. the more bands involved in a classification problem, the more accurate results we can get.
  • Perform texture analysis (GLCM). Different features have different textures. Sometimes, looking only at a SAR image is hard to detect different features. When looking at their texture, then the interpretation of each feature improves.
  • Combination of optical and SAR data. This is a good choice if you want to achieve an accurate classification.

The more layers are available, the better. Hence, you can combine the following layers:

  • SAR dual-pol data
  • Optical data
  • Texture analysis

If you want guidance on how to perform supervised classification, you can have a look at the following posts:

I hope that helps

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