Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity

Abstract

Remote sensing change detection (RSCD) is an important yet challenging task in Earth observation. The booming development of convolutional neural networks (CNNs) in computer vision raises new possibilities for RSCD, and many recent RSCD methods have introduced CNNs to achieve promising improvements in performance. In this paper we propose a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images. First, we propose an elaborate feature fusion module consisting of a multidirectional fusion pathway (MFP) and an adaptive weighted fusion (AWF) strategy for RSCD to boost the way that information propagates in the network. The MFP enhances the flexibility and diversity of information paths by creating extra top-down and shortcut-connection paths. The AWF strategy conducts weight recalibration for every fusion node to highlight salient feature maps and overcome semantic gaps between different features. Second, a novel perceptual similarity module is designed to introduce perceptual loss into the RSCD task, which adds perceptual information, such as structure and semantic information, for high-quality change map generation. Extensive experiments on four challenging benchmark datasets demonstrate the superiority of the proposed network compared with eight state-of-the-art methods in terms of F1, Kappa, and visual qualities.