Normalized multi-end-member decomposition hyperspectral reconstruction method in pixel unmixing inverse process

Abstract

The invention relates to a normalized multi-end-member decomposition hyperspectral reconstruction method in a pixel unmixing inverse process. The method is characterized by comprising the following steps that: a reflectivity image of a multispectral image is subjected to normalized multi-end-member decomposition to obtain hyperspectral data; and a ground object spectrum extracted from the multispectral image can be decomposed into two intrinsic linear combinations of the spectral shape and the pixel DN value. The normalized multi-end-member decomposition hyperspectral reconstruction method is characterized in that the mixing in different properties is carried out according pure end members in a spectral library to obtain the optimum end member components in a mixed scene, so phenomena of noise amplification due to too many end members and the precision reduction due to too few end members can be avoided; the temporal and spatial variation of the end members is considered on the precise unmixing basis; and the hyperspectral data is accurately reconstructed when the calculation quantity is reduced. Through the spectral reconstruction on the multispectral data, the continuous hyperspectral data can be obtained; and the high space resolution and the high signal-to-noise ratio of the multispectral image are remained, and meanwhile, the spectral resolution of the multispectral data is improved.

Description

technical field [0001] The invention relates to the technical field of remote sensing, in particular to a method for reconstructing a corresponding hyperspectral image by performing normalized multi-terminal decomposition on a multispectral image during the inverse process of mixed pixel unmixing. Background technique [0002] Hyperspectral remote sensing data can provide continuous spectra of ground objects with a spectral resolution of less than ten nanometers, while ordinary multispectral data has a spectral resolution of hundreds of nanometers, making hyperspectral data available for finer classification and quantitative processing. However, it is difficult to obtain hyperspectral data, and the cost is relatively high, and only a small area of ​​hyperspectral data can be obtained (for example, Hyperion data is only a dozen kilometers wide); and the spatial, spectral resolution and signal-to-noise ratio cannot be simultaneously It is guaranteed that the acquisition of hig...

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Problems solved by technology

However, it is difficult to obtain hyperspectral data, and the cost is relatively high, and only a small area of ​​hyperspectral data can be obtained (for example, Hyperion data is only a dozen kilometers wide); and the spatial, spectral resolution and signal-to-noise ratio cannot be simultaneously It is guaranteed that the acquisition of high spectral resolution and spatial resolution images will reduce the signal-to-noise ratio of the image, which will reduce the quality of the acquired data; when obtaining high spectral resolution while ensuring a high signal-to-noise ratio, the spatial resolution must be reduced Rate

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