Local and Global Feature Learning With Kernel Scale-Adaptive Attention Network for VHR Remote Sensing Change Detection

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2022, Vol.15, p.7308-7322
• Main Authors: Lei, Tao, Xue, Dinghua, Ning, Hailong, Yang, Shuangming, Lv, Zhiyong, Nandi, Asoke K.
• Format: Article
• Language: English
• Subjects: Ablation; Attention mechanism; Change detection; Convolution; Detection; Electronic mail; Embedding; Feature extraction; Feature maps; Flexibility; Kernel; Kernels; Learning; Modules; multi- layer perceptron; Multilayer perceptrons; Remote sensing; Semantics; skip-connection; Spatial discrimination learning; Task analysis
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2022.3200997

Change detection is an important task of identifying changed information by comparing bitemporal images over the same geographical area. Currently, many existing methods based on U-Net and attention mechanism have greatly promoted the development of change detection techniques. However, they still suffer from two main challenges. First, faced with the diversity of ground objects and the flexibility of scale changes, vanilla attention mechanisms cripple spatial flexibility in learning object details due to the same scale convolution kernels at different convolution layers. Second, the complex background and high similarity between changed information and nonchanged information makes it difficult to fuse low-level details and high-level semantic by simple skip-connection in U-Net. To address the above issues, a local and global feature learning with kernel scale-adaptive attention network (LGSAA-Net) is proposed in this article. The proposed network makes two contributions. First, a scale-adaptive attention (SAA) module has been designed to exploit the relationships between feature maps and convolutional kernel scales. The SAA module can achieve better feature discrimination than vanilla attention mechanism. Second, a multilayer perceptron based on patches embedding has been employed by skip-connection to learn the local and global pixel association, which is helpful for achieving globally deep fusion of low-level details and high-level semantics. Finally, experiments and ablation studies are conducted on three datasets of LEVIR/WHU/GZ. Experimental results demonstrate that the proposed LGSAA-Net performs favorably against comparative current approaches and provides more accurate contour and better internal compactness for changed targets, thus verifying the effectiveness and superiority of the proposed LGSAA-Net in VHR remote sensing change detection.