LAUSR

Joint classification using multisource remote sensing data for Earth observation is promising but challenging. Due to the gap of imaging mechanism and imbalanced information between multisource data, integrating the complementary merits for interpretation is still full of difficulties. In this article, a classification method based on asymmetric feature fusion, named asymmetric feature fusion network (AsyFFNet), is proposed. First, the weight-share residual blocks are utilized for feature extraction while keeping separate batch normalization (BN) layers. In the training phase, redundancy of the current channel is self-determined by the scaling factors in BN, which is replaced by another channel when the scaling factor is less than a threshold. To eliminate unnecessary channels and improve the generalization, a sparse constraint is imposed on partial scaling factors. Besides, a feature calibration module is designed to exploit the spatial dependence of multisource features, so that the discrimination capability is enhanced. Experimental results on the three datasets demonstrate that the proposed AsyFFNet significantly outperforms other competitive approaches.