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Hyperspectral image abnormal target detection method based on low rank and sparse decomposition

A hyperspectral image and sparse decomposition technology, applied in the field of image anomaly detection, can solve the problem of many parameters, achieve the effect of fewer parameters, improve computing efficiency and accuracy, and simplify settings

Active Publication Date: 2020-04-21
中国人民解放军火箭军工程大学
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Problems solved by technology

This method needs to set more parameters, and there is still room for improvement in detection accuracy and running time

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  • Hyperspectral image abnormal target detection method based on low rank and sparse decomposition

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Embodiment Construction

[0036] Embodiments of the present invention are described below with reference to the drawings, in which like parts are denoted by like reference numerals. In the case of no conflict, the following embodiments and the technical features in the embodiments can be combined with each other.

[0037] Figure 1-2 A flow diagram of the method of the invention is shown. The method of the invention comprises the following steps: converting hyperspectral image data into a two-dimensional matrix; performing low-rank and sparse matrix decomposition; calculating a covariance matrix; traversing the entire image by using a sliding window to obtain detection results. Detailed description will be given below.

[0038] refer to figure 1 , in S1, convert the hyperspectral image data into a two-dimensional matrix.

[0039] For a hyperspectral image X with a size of m×n×p, it is converted into a two-dimensional N×p matrix X. Among them, m represents the total number of rows in the hyperspect...

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Abstract

The invention belongs to the technical field of remote sensing image processing, and discloses a hyperspectral image abnormal target detection method based on low rank and sparse decomposition. The method comprises the steps: S1, converting a hyperspectral image into a two-dimensional matrix; S2, performing low-rank and sparse matrix decomposition on the two-dimensional matrix by using the low-rank characteristic of the background in the image and the sparse characteristic of the target to obtain a low-rank matrix and a sparse matrix; S3, for the low-rank background matrix, calculating the mean value of the low-rank background matrix, and then calculating a global covariance matrix; and S4, traversing the hyperspectral image by using double sliding windows to obtain a detection result. Themethod has the characteristics of simplicity, quickness and high precision, and has good practical value in the aspects of quality detection, environmental monitoring, military reconnaissance and thelike by adopting a hyperspectral technology.

Description

technical field [0001] The method of the present invention relates to the technical field of image anomaly detection, in particular to a hyperspectral image anomaly target detection method based on low-rank and sparse decomposition. Background technique [0002] Hyperspectral image abnormal target detection is a popular and key technology in the field of hyperspectral image processing. Its essence is a binary classification technology, which divides hyperspectral images into two parts: the background and the anomalies that are different from the background. It can effectively detect anomalous objects in hyperspectral images without any prior information. Because of its characteristics of no application conditions, it has attracted wide attention, and has great application value and broad application prospects in environmental monitoring, mineral detection, food quality inspection and military reconnaissance. [0003] At present, the most classic algorithm in the field of hy...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V20/20G06V2201/07Y02A40/10
Inventor 王涛常红伟苏延召姜柯韩德帅曹继平
Owner 中国人民解放军火箭军工程大学
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