PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine
This work addresses the problem of interpreting PolSAR images for remote sensing applications, but it appears incremental as it builds on existing techniques without introducing a major breakthrough.
The paper tackles PolSAR image classification by proposing a method that combines polarimetric scattering coding to create sparse matrices and a sparse support matrix machine for classification, achieving better results than existing methods.
POLSAR image has an advantage over optical image because it can be acquired independently of cloud cover and solar illumination. PolSAR image classification is a hot and valuable topic for the interpretation of POLSAR image. In this paper, a novel POLSAR image classification method is proposed based on polarimetric scattering coding and sparse support matrix machine. First, we transform the original POLSAR data to get a real value matrix by the polarimetric scattering coding, which is called polarimetric scattering matrix and is a sparse matrix. Second, the sparse support matrix machine is used to classify the sparse polarimetric scattering matrix and get the classification map. The combination of these two steps takes full account of the characteristics of POLSAR. The experimental results show that the proposed method can get better results and is an effective classification method.