Study on ground object classification based on the hyperspectral fusion images of ZY-1(02D) satellite

Spaceborne hyperspectral images are expected to have abundant applications in various fields, particularly in the quantitative observation of the Earth. However, the problem of low spatial resolution has limited their effectiveness to some extent. There are urgent needs for high-spatial-resolution hyperspectral images. We explore fusing the hyperspectral image with panchromatic and multispectral images captured by sensors onboard ZY-1 (02D) to obtain high-spatial-resolution and high-spectral-resolution images. Four approaches to obtain the final high-spatial-resolution hyperspectral image data are proposed, and six fusion methods are used. The fusion results were evaluated using quantitative indicators and classification performance, and image quality greatly improved after fusion. The fusion approach that used multispectral image data as an intermediate layer twice in the fusion process obtained the best fusion effect, and this was verified by both quantitative and application evaluation results. This provides an effective approach to obtain a high-quality, high-spatial-resolution hyperspectral image using the combination of panchromatic, multispectral, and hyperspectral images acquired by the sensors onboard ZY-1(02D).