Abstract
Hyperspectral imaging has been rapidly developing over the past decade, and modern sensor technologies can cover large areas with exceptional spatial, spectral, and temporal resolutions. Due to these features, hyperspectral imaging is used effectively in numerous remote sensing applications such as precision agriculture, environmental monitoring, food analysis, and military applications requiring estimation of physical parameters of many complex surfaces and identifying visually similar materials with acceptable spectral signatures. The scope of fusion of the two images, one with high spatial content and the other with high spectral content, is to estimate one image with high spatial and spectral content. This paper presents a brief review of recent image resolution fusion algorithms, including deep learning techniques, for hyperspectral images. The need of high resolution panchromatic (pan) and multispectral (MS) images, lossless registration of images from multiple sources and point spread function (PSF) impose limitations for performing pan sharpening process. Hence the fusion is achieved using multispectral images instead of panchromatic images on hyperspectral images. It is essential to identify and reduce uncertainties in the image processing chain to improve image fusion enhancement. This paper also presents the current practices, problems, and prospects of hyperspectral image fusion. In addition, some important issues affecting fusion performance are discussed.
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Sara, D., Mandava, A.K., Kumar, A. et al. Hyperspectral and multispectral image fusion techniques for high resolution applications: a review. Earth Sci Inform 14, 1685–1705 (2021). https://doi.org/10.1007/s12145-021-00621-6
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