| Autor: |
Yihao Wang, Jianyu Chen, Xuanqin Mou, Tieqiao Chen, Junyu Chen, Jia Liu, Xiangpeng Feng, Haiwei Li, Geng Zhang, Shuang Wang, Siyuan Li, Yupeng Liu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Remote Sensing, Vol 16, Iss 7, p 1248 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs16071248 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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