Sparse unmixing method based on optical calculation

A sparse unmixing and optical computing technology, applied in the field of hyperspectral unmixing, can solve the problems of difficult real-time endmember unmixing and abundance matrix reconstruction, and achieve the effect of fast and effective sparse decomposition, fast response ability, and large storage capacity

Inactive Publication Date: 2017-05-31
CHONGQING TECH & BUSINESS UNIV
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Problems solved by technology

[0005] Disadvantages: The current hyperspectral sparse unmixing technology, due to the high computational complexity in the processing process, has problems such as real-time endmember unmixing and abundance matrix reconstruction.

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  • Sparse unmixing method based on optical calculation
  • Sparse unmixing method based on optical calculation
  • Sparse unmixing method based on optical calculation

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Embodiment Construction

[0028] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0029] figure 1 is the flow chart of hyperspectral sparse unmixing based on optical calculation, figure 2 is an optical 4f system, image 3 is a joint transform correlator. Such as figure 1 Shown: a kind of hyperspectral sparse unmixing method based on optical calculation provided by the present invention, comprises the following steps:

[0030] The first step: using the optical filtering method to obtain the transform domain sparse coefficient matrix of the hyperspectral signal;

[0031] Obtaining the transformation domain sparse coefficient matrix of the hyperspectral signal by the optical filtering method includes an optical multi-scale transformation step, and the optical multi-scale transformation includes:

[0032] 1) Optical non-adaptive multi-scale transformation (represented by contourlet), which specifically includes the fol...

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Abstract

The invention relates to a sparse unmixing method based on optical calculation. The method comprises the following steps: S1, an optical filtering method is adopted to acquire a transform domain sparse coefficient matrix for hyperspectral signals; S2, a deterministic measurement matrix is designed to realize perception measurement on the hyperspectral signals; S3, parallel multiplication calculation is carried out on the sparse coefficient matrix and the deterministic measurement matrix through a hardware FPGA for acquiring endmember signals; and S4, the sparse structural information of the hyperspectral signals and a structured reconstruction algorithm are used for reconstructing an abundance matrix. The optical filtering technology and an electrical system are adopted to realize multiscale geometric analysis, the sparse unmixing efficiency is greatly improved on the premise of ensuring the reconstruction precision, and quick and effective sparse decomposition can be realized.

Description

technical field [0001] The invention relates to a hyperspectral unmixing technology, in particular to a novel sparse unmixing method based on optical calculation. Background technique [0002] Due to the diversity of types of measured objects, real-time changes of ground objects over time, and multiple scattering of ground object spectra, it is inevitable that there will be mixed pixels in the sampling process of hyperspectral sensors. In the research of hyperspectral image classification, how to obtain the real attribute information at sub-pixel accuracy and improve the accuracy of spectral image classification, especially in the premise of high real-time system requirements, massive remote sensing data and high fidelity requirements for reconstructed image quality It has important academic value and has very practical research significance in the fields of sub-pixel target detection, fine rock and mineral identification, and ground camouflage identification. [0003] Ther...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B10/11H04B10/516H04B10/80G06K9/62
CPCH04B10/11H04B10/516H04B10/80G06F18/2136
Inventor 徐速
Owner CHONGQING TECH & BUSINESS UNIV
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