Space-spectral joint multi-constraint optimization non-negative matrix unmixing method

Abstract

The invention discloses a non-negative matrix unmixing method for space-spectrum combined multi-constraint optimization, the steps of which are: 1) Estimating the number of hyperspectral endmembers; 2) Constructing the minimum distance constraint item of the endmember spectrum; number sparsity constraint item; 4) Construct the abundance map gradient domain group sparsity constraint item; 5) Establish the space-spectrum joint multi-constraint optimization non-negative matrix unmixing model; 6) Alternate direction iterative solution; 7) Output the terminal of the unmixed result Yuan and abundance map. The present invention makes full use of the minimum distance between the endmember spectrum and the geometric center of mass of the hyperspectral image, the abundance sparsity and the smoothness of the slice, and limits the search space of the endmember and abundance solution through multiple constraints, avoids local minimum, and obtains the optimal solution through iterative solution. solution; compared with the traditional classical non-negative matrix unmixing model method, the present invention improves the accuracy of unmixing, enhances the robustness of the method to noise, and can be widely used in hyperspectral unmixing in the fields of land resources, mineral exploration and precision agriculture. Supervised unmixing.

Description

technical field [0001] The invention relates to remote sensing hyperspectral data processing technology, in particular to a space-spectrum joint multi-constraint optimization non-negative matrix unmixing method. Background technique [0002] Due to its spectral correlation and rich spatial information, hyperspectral data are widely used in military monitoring, precision agriculture, and mineral exploration. Among them, hyperspectral data unmixing is the key technology of quantitative remote sensing analysis. The basic principle of hyperspectral data unmixing is to decompose a single pixel spectrum into a combination of several pure pixel (end member) spectra. The theoretical basis is that due to the limitation of the spatial resolution of the imaging spectrometer, there are a large number of mixed pixels in the obtained hyperspectral image, and each mixed pixel contains a variety of pure substances (ie, end members). [0003] Many unmixing algorithms for hyperspectral data...

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Problems solved by technology

However, the above method only utilizes the correlation of hyperspectral spectral information, does not effectively combine spatial-spectral information, the unmixing accuracy is low, and the performance of the algorithm decreases when there is noise in the data

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