Polarimetric Convolutional Network for PolSAR Image Classification
This work addresses the challenge of improving classification accuracy for PolSAR images, which is important for remote sensing applications, but it appears incremental as it builds on existing convolutional network methods with a new encoding technique.
The authors tackled the problem of limited polarimetric information in PolSAR image classification by proposing a sparse scattering coding method to extract a more complete feature set, and designed a convolutional network to classify PolSAR images, achieving better results as demonstrated on AIRSAR and RADARSAT-2 datasets.
The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix only show a limited number of polarimetric information. In order to solve this problem, we propose a sparse scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on convolution network to combine this feature. Based on sparse scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for sparse scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding.