Difference Enhancement and Spatial-Spectral Nonlocal Network for Change Detection in VHR Remote Sensing Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 13
• Main Authors: Lei, Tao, Wang, Jie, Ning, Hailong, Wang, Xingwu, Xue, Dinghua, Wang, Qi, Nandi, Asoke K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Change detection; Change detection (CD); Clustering algorithms; Coders; Computer applications; Computing costs; Convolution; Detection; difference enhancement (DE) module; Feature extraction; Image edge detection; Image enhancement; Image resolution; Information processing; Modules; Neural networks; Remote sensing; Robustness; Siamese convolutional neural networks (CNNs); spatial-spectral nonlocal (SSN) module; Spectra; Task analysis; Tightness
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2021.3134691

The popular Siamese convolutional neural networks (CNNs) for remote sensing (RS) image change detection (CD) often suffer from two problems. First, they either ignore the original information of bitemporal images or insufficiently utilize the difference information between bitemporal images, which leads to the low tightness of the changed objects. Second, Siamese CNNs always employ dual-branch encoders for CD, which increases computational cost. To address the above issues, this article proposes a network based on difference enhancement and spatial-spectral nonlocal (DESSN) for CD in very-high-resolution (VHR) images. This article makes threefold contributions. First, we design a difference enhancement (DE) module that can effectively learn the difference representation between foreground and background to reduce the impact of irrelevant changes on the detection results. Second, we present a spatial-spectral nonlocal (SSN) module that is different from vanilla nonlocal because multiscale spatial global features are incorporated to model the large-scale variation of objects during CD. The module can be used to strengthen the edge integrity and internal tightness of changed objects. Third, the asymmetric double convolution with Ghost (ADCG) module is exploited instead of standard convolution. The ADCG can not only refine the edge information of the changed objects, since horizontal and vertical convolutional kernels have good contour preservation advantages, but also greatly reduce the computational complexity of the proposed model. The experiments on two public VHR CD datasets demonstrate that the proposed network can provide higher detection accuracy and requires smaller memory usage than state-of-the-art networks.