GPU-based parallel realizing method of abundance estimation algorithm

Abstract

The invention discloses a GPU-based parallel realizing method of an abundance estimation algorithm. The GPU-based parallel realizing method comprises the steps of: 1, storing hyperspectral mixed pixeldata H to a GPU end; 2, acquiring end element vectors m, and allocating the acquired end element vectors m to p parallel threads on a CPU side one by one; 3, acquiring vectors m<~> which correspond to the end element vectors m and are orthogonal in a vector space [m<1>, m<2>, ..., m<i-1>]; 4, acquiring an orthogonal basis corresponding to an end element set M, and calculating a corresponding inner product value; 5, calculating projection of a spectral vector of each pixel in a d<~T> direction; 6, and calculating an abundance result alpha p corresponding to each end element mp, andstoring the abundance result alpha p to the GPU end. According to the GPU-based parallel realizing method, the abundance value is calculated by adopting a GPU parallel processing kernel function, theparallel mechanism runs much faster than on the traditional CPU serial mechanism, and the operating efficiency of real-time abundance estimation is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a GPU parallel processing method for hyperspectral image abundance value estimation methods, so as to improve fast calculation of real-time hyperspectral abundance. Background technique [0002] Hyperspectral remote sensing technology aims to obtain ground object information in tens to hundreds of continuous fine spectral bands, and has been widely used in many fields such as environment, national defense, agriculture, and deep space. Abundance estimation is one of the most important topics in hyperspectral technology. Its purpose is to obtain the corresponding percentage (ie, abundance value) of the corresponding endmember in each mixed pixel after the endmember extraction is completed. The implementation of traditional algorithms (such as least square error and orthogonal subspace projection) usually involves a large number of matrix inversion and multiplic...

AI Technical Summary

Problems solved by technology

The implementation of traditional algorithms (such as least square error and orthogonal subspace projection) usually involves a large number of matrix inversion and multiplication operations, making it slow in software operation and difficult to implement in hardware, so it is not suitable for Applications with high real-time requirements

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