| Paper ID | IVMSP-27.5 |
| Paper Title |
AN ADAPTIVE MULTI-SCALE AND MULTI-LEVEL FEATURES FUSION NETWORK WITH PERCEPTUAL LOSS FOR CHANGE DETECTION |
| Authors |
Jialang Xu, Yang Luo, University of Electronic Science and Technology of China, China; Xinyue Chen, Sichuan University, China; Chunbo Luo, University of Electronic Science and Technology of China, China |
| Session | IVMSP-27: Multi-modal Signal Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
