Change detection on multi-sensor imagery using mixed interleaved group convolutional network

Abstract

The difference of the spatio-spectral features of multi-sensor image causes big difficulty in change detection because of the difficulties of the feature extraction. Unlike the traditional approaches that mainly relying on manually feature design, the advances of deep learning-based methods in deep feature extraction provide new alternatives for multi-sensor imagery change detection. Specifically, the incorporation of multi-scale information from remote sensing images holds paramount importance in change detection, consistently applied in the design of various deep learning models. This study investigated a change detection approach utilizing a mixed interleaved group convolutional network (MIGCNet) on multi-sensor remote sensing imagery, with a specific focus on fine-grained kernel space and multi-scale feature analysis within convolution operations. The proposed MIGCNet, with parallel branches as the fundamental architecture, can distinguish the change information effectively by the proposed mixed interleaved group convolution (MIGC) module, which combined mixed convolution with interleaved group convolution. Meanwhile, multi-loss supervision is utilized to promote the performance of the proposed MIGCNet. Experimental results demonstrate the outperformance of the MIGCNet to handle change detection with multi-sensor images on urban area. Considering different datasets, the Overall Accuracy and Kappa Coefficient are reaching 0.97 and 80.67%, respectively, and the miss detection rate and the false alarm rate are as low as 0.17 and 0.18, respectively.