FEATURE EXCHANGE FOR MULTISOURCE DATA CLASSIFICATION IN WETLAND SCENE

Wetland classification is of great significance for monitoring. Recently, collaborative analysis of multisource data has received special attention considering the limitations of single source data. In this paper, a wetland classification method based on feature exchange is proposed. Firstly, the weighting shared residual blocks are utilized for feature extraction. Then, the scaling factors in batch normalization (BN) self-determine the redundancy of current channel, which is replaced by another channel when the scaling factor is less than the threshold. To eliminate unnecessary channels and improve the generalization, sparsity constraint is employed on partial scaling factors. Experimental results on multisource wetland dataset demonstrate that the proposed method outperforms other competitive works.