Fusion of Hyperspectral and Multispectral Images with Radiance Extreme Area Compensation

Author:

Wang Yihao1234,Chen Jianyu4,Mou Xuanqin2,Chen Tieqiao13,Chen Junyu13,Liu Jia1345,Feng Xiangpeng1,Li Haiwei1ORCID,Zhang Geng1,Wang Shuang1,Li Siyuan1,Liu Yupeng5

Affiliation:

1. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

2. Institute of Image Processing and Pattern Recognition, School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China

3. University of Chinese Academy of Sciences, Beijing 100049, China

4. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

5. State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China

Abstract

Although the fusion of multispectral (MS) and hyperspectral (HS) images in remote sensing has become relatively mature, and different types of fusion methods have their own characteristics in terms of fusion effect, data dependency, and computational efficiency, few studies have focused on the impact of radiance extreme areas, which widely exist in real remotely sensed scenes. To this end, this paper proposed a novel method called radiance extreme area compensation fusion (RECF). Based on the architecture of spectral unmixing fusion, our method uses the reconstruction of error map to construct local smoothing constraints during unmixing and utilizes the nearest-neighbor multispectral data to achieve optimal replacement compensation, thereby eliminating the impact of overexposed and underexposed areas in hyperspectral data on the fusion effect. We compared the RECF method with 11 previous published methods on three sets of airborne hyperspectral datasets and HJ2 satellite hyperspectral data and quantitatively evaluated them using 5 metrics, including PSNR and SAM. On the test dataset with extreme radiance interference, the proposed RECF method achieved well in the overall evaluation results; for instance, the PSNR metric reached 47.6076 and SAM reached 0.5964 on the Xiong’an dataset. In addition, the result shows that our method also achieved better visual effects on both simulation and real datasets.

Publisher

MDPI AG

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