DOI: 10.3390/rs12030484
论文题名: PGA-SiamNet: Pyramid feature-based attention-guided siamese network for remote sensing orthoimagery building change detection
作者: Jiang H. ; Hu X. ; Li K. ; Zhang J. ; Gong J. ; Zhang M.
刊名: Remote Sensing
ISSN: 20724292
出版年: 2020
卷: 12, 期: 3 语种: 英语
英文关键词: Attention mechanism
; Building change detection
; Remote sensing orthoimagery
; Siamese convolutional neural network
Scopus关键词: Buildings
; Convolution
; Deep learning
; Remote sensing
; Semantics
; Attention mechanisms
; Building change detection
; End-to-end network
; High resolution remote sensing
; Long-range dependencies
; Object information
; Orthoimagery
; State-of-the-art methods
; Convolutional neural networks
英文摘要: 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. © 2020 by the authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159534
Appears in Collections: 气候变化与战略
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作者单位: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China; Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China
Recommended Citation:
Jiang H.,Hu X.,Li K.,et al. PGA-SiamNet: Pyramid feature-based attention-guided siamese network for remote sensing orthoimagery building change detection[J]. Remote Sensing,2020-01-01,12(3)