Paper Info
Title
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.
Year
DOI
Venue
2020
10.3390/rs12030484
REMOTE SENSING
Keywords
Field
DocType
building change detection,remote sensing orthoimagery,attention mechanism,Siamese convolutional neural network
Computer vision,Change detection,Remote sensing,Pyramid,Artificial intelligence,Feature based,Geology,Orthophoto
Journal
Volume
Issue
Citations 
12
3
3
PageRank 
References 
Authors
0.37
0
6
Name
Order
Citations
PageRank
Huiwei Jiang130.71
Xiangyun Hu2798.87
Kun Li330.71
Jinming Zhang430.71
Jinqi Gong541.10
Mi Zhang631.05