Hyperspectral image acquisition method based on very few of optimization selection wavebands

Abstract

The invention provides a hyperspectral image acquisition method based on a very few of optimization selection wavebands. The method includes the steps that firstly, high-definition images with a very few of optimization selection wavebands are obtained; then low-definition hyperspectral image sets with ground feature classes similar to those of the obtained high-definition images are selected, dictionary pre-training is carried out, and an over-complete spectrum dictionary is obtained; then the high-definition images with a very few of optimization selection wavebands are subjected to sparse representation under the nothing but negative constraint condition, and a sparse representation coefficient is obtained; finally, hyperspectral images with high definition are obtained through the dictionary and the spare coefficient. The obtained high-definition images with a very few of wavebands contain hyperspectral important waveband information, and it is possible to obtain high-quality spectrums; dependence on hyperspectral images under the same scene is avoided, and the data acquisition difficulty is reduced; the defects of end member decomposition on the descriptive power of spectral characteristics of the hyperspectral images are overcome, and the precision of obtained spectrums and the obtaining effectiveness are effectively improved.

Description

technical field [0001] The invention belongs to the field of image processing, is applicable to hyperspectral remote sensing image reconstruction, and specifically relates to a hyperspectral image acquisition method based on a very small number of optimally selected bands. Background technique [0002] Hyperspectral images are composed of a large number of single-band images, and each pixel in the image has a quasi-continuous spectral curve. In the hyperspectral imaging process, due to the narrow spectral bandwidth, a large instantaneous field of view (IFOR) must be used to accumulate enough light quanta to maintain the signal-to-noise ratio of imaging, and the increase of the instantaneous field of view will reduce the resolution of the image. . However, in many application fields of hyperspectral images, such as object recognition and classification, and environment detection, high-resolution images are indispensable, so it is of great significance to obtain hyperspectral...

AI Technical Summary

Problems solved by technology

However, there are certain difficulties in the number of endmembers and the extraction of pure endmembers in the decomposition of mixed pixels, so the obtained hyperspectral image has spectral distortion, and it is impossible to obtain satisfactory results in high-resolution hyperspectral image reconstruction. result

And hyperspectral images in the same scene are often difficult to obtain, making this method difficult to generalize

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