PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection

Abstract

In recent years, building change detection has made remarkable progress through using deep learning. The core problems of this technique are the need for additional data (e.g., Lidar or semantic labels) and the difficulty in extracting sufficient features. In this paper, we propose an end-to-end network, called the pyramid feature-based attention-guided Siamese network (PGA-SiamNet), to solve these problems. The network is trained to capture possible changes using a convolutional neural network in a pyramid. It emphasizes the importance of correlation among the input feature pairs by introducing a global co-attention mechanism. Furthermore, we effectively improved the long-range dependencies of the features by utilizing various attention mechanisms and then aggregating the features of the low-level and co-attention level; this helps to obtain richer object information. Finally, we evaluated our method with a publicly available dataset (WHU) building dataset and a new dataset (EV-CD) building dataset. The experiments demonstrate that the proposed method is effective for building change detection and outperforms the existing state-of-the-art methods on high-resolution remote sensing orthoimages in various metrics.

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

“…Specifically, the most commonly used types of multispectral images for AI-based change detection methods are derived from the Landsat series of satellites and the Sentinel series of satellites [66,67], due to their low acquisition cost and high time and space coverage. In addition, other satellites, such as Quickbird [68][69][70][71][72][73][74], SPOT series [75][76][77][78], Gaofen series [14,79,80], Worldview series [81][82][83][84][85], provide high and very high spatial resolution images, and various aircrafts provide very high spatial resolution aerial images [20,[86][87][88][89][90][91][92][93][94], allowing the change detection results to retain more details of the changes. HSIs have hundreds or even thousands of continuous and narrow bands, which can provide abundant spectral and spatial information.…”

“…According to whether the weights of sub-networks are shared, this can be divided into the pure-Siamese structure [22,68,94,117,155,156] and the pseudo-Siamese structure [79,109,157,158]. The main difference is that the former sub-network extracts the common features of the two-period data by sharing weights.…”

“…Satellite images [52,228] b. SAR images [229] Public space management Street view images [117] Building change detection a. Aerial images [86,89,90] b. Satellite images [85,192] c. Satellite/Aerial images [88,94] d. Airborne laser scanning data and aerial images [91] e. SAR images [112] f. Satellite images and GIS map [177] Resources & environment Human-driven environmental changes Satellite images [69] Hydro-environmental changes Satellite images [56] Sea ice SAR images [171] Surface water Satellite images [230,231] Forest monitoring Satellite images [45,63,150,196,232] Natural disasters…”

“…In [89], the authors proposed two CNN models, a mask R-CNN for object-based instance segmentation and a multi-scale CNN for pixel-based semantic segmentation, and their intersection of union (IoU) accuracy of buildings was more than 0.83. In [94], the authors proposed a pyramid feature-based attention-guided Siamese network to detect building changes and the IoU of change map exceeded 0.97. Sometimes, the problem of constant cloud coverage prevents optical RS images from being fully utilized, and SAR data represent a good alternative [112].…”

Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis

“…However, the performance of existing WSL-based methods in remote sensing images is still far from satisfactory. For example, accurate position of the change cannot be yielded in detection of building changes [130]. Much effort also needs to be made to establish more efficient methods to improve the detection accuracy [126].…”