期刊
REMOTE SENSING
卷 14, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/rs14040850
关键词
coastal wetlands; multisource remote sensing; land-cover mapping; biodiversity estimation; spatial-spectral vision transformer
类别
资金
- Beijing Natural Science Foundation [JQ20021]
- National Natural Science Foundation of China [61922013, 62001023]
- China Postdoctoral Science Foundation [BX20200058]
This paper proposes a systematic framework for multisource remote sensing image processing, which fuses hyperspectral and multispectral images and utilizes a spatial-spectral vision transformer to extract sequential relationships, achieving accurate land-cover mapping.
The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) are fused by the CNN-based method to obtain the fused image with high spatial-spectral resolution. Secondly, considering the sequentiality of spatial distribution and spectral response, the spatial-spectral vision transformer (SSViT) is designed to extract sequential relationships from the fused images. After that, an external attention module is utilized for feature integration, and then the pixel-wise prediction is achieved for land-cover mapping. Finally, land-cover mapping and benthos data at the sites are analyzed consistently to reveal the distribution rule of benthos. Experiments on ZiYuan1-02D data of the Yellow River estuary wetland are conducted to demonstrate the effectiveness of the proposed framework compared with several related methods.
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