Title
Multi-Resolution Collaborative Fusion of SAR, Multispectral and Hyperspectral Images for Coastal Wetlands Mapping
Abstract
The hyperspectral, multispectral, and synthetic aperture radar (SAR) remote sensing images provide complementary advantages in high spectral resolution, high spatial resolution, and geometric and polarimetric properties, generally. How to effectively integrate cross-modal information to obtain a high spatial resolution hyperspectral image with the characteristics of the SAR is promising. However, due to divergent imaging mechanisms of modalities, existing SAR and optical image fusion techniques generally remain limited due to the spectral or spatial distortions, especially for complex surface features such as coastal wetlands. This paper provides, for the first time, an efficient multi-resolution collaborative fusion method for multispectral, hyperspectral, and SAR images. We improve generic multi-resolution analysis with spectral-spatial weighted modulation and spectral compensation to achieve minimal spectral loss. The backscattering gradients of SAR are guided to fuse, which is calculated from saliency gradients with edge preserving. The experiments were performed on ZiYuan-1 02D (ZY-1 02D) and GaoFen-5B (AHSI) hyperspectral, Sentinel-2 and GaoFen-5B (VIMI) multispectral, and Sentinel-1 SAR images in the challenging coastal wetlands. Specifically, the fusion results were comprehensively tested and verified on the qualitative, quantitative, and classification metrics. The experimental results show the competitive performance of the proposed method.
Year
DOI
Venue
2022
10.3390/rs14143492
REMOTE SENSING
Keywords
DocType
Volume
remote sensing, hyperspectral, ZY-1 02D, GaoFen-5, synthetic aperture radar, data fusion, pixel-level, coastal wetlands, classification
Journal
14
Issue
ISSN
Citations 
14
2072-4292
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yi Yuan100.34
Xiangchao Meng202.03
Weiwei Sun315.75
Gang Yang426.10
Lihua Wang59312.44
Jiangtao Peng615.42
Yumiao Wang700.34