Adaptive weighted learning for vegetation contribution in soil moisture inversion using PolSAR data

The high-resolution soil moisture inversion under vegetation from remote-sensing data is a challenging task. The key issue is to separate or eliminate the influence of vegetation. Therefore, the difficult problem is to select appropriate vegetation descriptors and accurately deal with them. With the great success of convolutional neural network (CNN) in PolSAR image classification, this paper intends to use CNN and introduce an adaptive weighted learning mechanism to calculate the new vegetation descriptors and eliminate the influence of vegetation on soil moisture inversion under vegetation by modifying the network structure. First, we propose an adaptive weighted learning module that can learn the adaptive weights of vegetation contribution which are the volume scattering components and the double-bounce scattering components in the Freeman-Durden decomposition to obtain new vegetation parameters. Second, we transform the inversion problem into a classification-regression problem, and use CNN to design two corresponding models to complete the inversion of soil moisture. In the proposed strategy, the adaptive weighted learning can effectively extract salient features from different components and combine them to obtain new vegetation descriptors and, the CNN can effectively utilize the spatial distribution of PolSAR data to automatically extract features that are useful for soil moisture inversion. The experimental results show that both classification network and retrospective network can achieve high inversion accuracy, whose inversion accuracy reaches up 96.66%, and root mean square error and the determination coefficient are 2.32% and 0.93, respectively. It suggests the great potential of combining the deep learning technique with traditional inversion model for soil moisture inversion from PolSAR data.