Sparse dual constrained hyperspectral image unmixing method

Abstract

The invention provides a sparse dual constrained hyperspectral image unmixing method, which comprises the following steps: S10, obtaining a hyperspectral image to be unmixed, and constructing a nearest neighbor graph by searching k nearest neighbor points of each pixel point in a high-dimensional data matrix; S20, taking each pixel point in the nearest neighbor graph constructed in the step S10 asa vector, and constructing another graph in the sparse region through a method of setting a sparse distance between the pixel points; S30, constructing two images according to the step S10 and the step S20; S40, decomposing the data matrix N layer by layer to obtain a corresponding matrix factor, adjusting the matrix factor by using a fine adjustment rule when the data matrix N is decomposed to the last layer, and performing iterative updating after adjustment; and S50, outputting a final iteration result, obtaining an end member matrix U and an abundance matrix V after the hyperspectral image is unmixed, and completing the unmixing of the hyperspectral image. The whole speed is high, the occupied storage space is small, a certain processing speed is maintained while the low complexity isensured, and the unmixing efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a hyperspectral image unmixing method. Background technique [0002] Hyperspectral remote sensing technology combines spectral technology and imaging technology to obtain multi-dimensional information while detecting the one-dimensional spectral information and two-dimensional collection space of the target, so as to obtain continuous and narrow-band image data with high spectral resolution. Generally speaking, the resolution of hyperspectral images is low and there are mixed pixels in the images. The processing process is much more difficult than pure pixels, but it has more practical application significance. Hyperspectral unmixing technology focuses on processing mixed pixels, and uses a relatively accurate classification technique to solve the proportion of mixed components distributed in mixed pixels, that is, to solve for abundance. In the process of unmixing, the...

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Problems solved by technology

[0011] In view of the above problems, the present invention provides a hyperspectral image unmixing method with sparse dual constraints, which effectively solves the technical problem of high complexity in hyperspectral image unmixing in the prior art

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