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Open AccessArticle

Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network

by 1,*, 1,*, 1,2 and 1
1
School of Computer Science, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech & Image Information Processing, Northwestern Polytechnical University, Xi’an 710129, China
2
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(23), 6735; https://doi.org/10.3390/s20236735
Received: 22 October 2020 / Revised: 12 November 2020 / Accepted: 18 November 2020 / Published: 25 November 2020
(This article belongs to the Section Remote Sensors)
Timely and accurate change detection on satellite images by using computer vision techniques has been attracting lots of research efforts in recent years. Existing approaches based on deep learning frameworks have achieved good performance for the task of change detection on satellite images. However, under the scenario of disjoint changed areas in various shapes on land surface, existing methods still have shortcomings in detecting all changed areas correctly and representing the changed areas boundary. To deal with these problems, we design a coarse-to-fine detection framework via a boundary-aware attentive network with a hybrid loss to detect the change in high resolution satellite images. Specifically, we first perform an attention guided encoder-decoder subnet to obtain the coarse change map of the bi-temporal image pairs, and then apply residual learning to obtain the refined change map. We also propose a hybrid loss to provide the supervision from pixel, patch, and map levels. Comprehensive experiments are conducted on two benchmark datasets: LEBEDEV and SZTAKI to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance. View Full-Text
Keywords: change detection; deep learning; attentive; coarse-to-fine; encoder-decoder architecture; end-to-end change detection; deep learning; attentive; coarse-to-fine; encoder-decoder architecture; end-to-end
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MDPI and ACS Style

Zhang, Y.; Zhang, S.; Li, Y.; Zhang, Y. Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network. Sensors 2020, 20, 6735. https://doi.org/10.3390/s20236735

AMA Style

Zhang Y, Zhang S, Li Y, Zhang Y. Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network. Sensors. 2020; 20(23):6735. https://doi.org/10.3390/s20236735

Chicago/Turabian Style

Zhang, Yi; Zhang, Shizhou; Li, Ying; Zhang, Yanning. 2020. "Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network" Sensors 20, no. 23: 6735. https://doi.org/10.3390/s20236735

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