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

Abstract

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 paper. 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 (MLPPE) 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

SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer

“…Zhi et al [25] proposed a novel neural network with a spatial-spectral attention mechanism and multi-scale dilation convolution modules, effectively alleviating the pseudo-changes caused by solar height and soil moisture in land cover CD for remote sensing images. To address the problems of feature diversity and scale-change flexibility, Lei et al [26] employed scale-adaptive attention to establish relationships between feature maps and convolution kernel scales, utilizing a multi-layer perceptron (MLP) that fuses low-level details and high-level semantics to improve feature discrimination. Wei et al [27] designed a location guidance module that accurately identifies changed regions.…”

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

“…For example, some researchers have used pre-trained image classification CNNs to extract change features [39]. Some researchers have used improved attention mechanisms for training networks to extract change information [40]. The third category uses some visual methods for unsupervised change detection based directly on images.…”

From Multiple Independent Metrics to Single Performance Measure Based on Objective Function

“…In data-driven decision making in any field one needs to establish the quality of an algorithm. It is extremely common in engineering and science to design algorithms to perform various tasks [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. For example, either in detection or classification using machine learning, one develops and compares many different algorithms.…”