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An Adaptive Multi-Scale and Multi-Level Features Fusion Network with Perceptual Loss for Change Detection

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Citation Author(s):
Jialang Xu, Yang Luo, Xinyue Chen, Chunbo Luo
Submitted by:
Jialang Xu
Last updated:
22 June 2021 - 1:49am
Document Type:
Presentation Slides
Document Year:
Presenters Name:
Jialang Xu
Paper Code:



Change detection plays a vital role in monitoring and analyzing temporal changes in Earth observation tasks. This paper proposes a novel adaptive multi-scale and multi-level features fusion network for change detection in very-high-resolution bi-temporal remote sensing images. The proposed approach has three advantages. Firstly, it excels in abstracting high-level representations empowered by a highly effective feature extraction module. Secondly, an elaborate feature fusion module incorporated with the channel and spatial attention mechanism is proposed to provide efficient fusion strategies for multi-scale and multi-level features from bi-temporal images and multiple convolutional layers. Finally, a novel perceptual auxiliary component is designed to capture the perceptual loss of the global perceptual and structural differences and address the optimization problem caused by only using per-pixel loss function in change detection. Comprehensive experiments on two benchmark datasets confirm that our proposed framework outperforms state-of-the-art algorithms in both quantitative assessment and visual interpretation.

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