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Matrix factorization-based hyperspectral image saliency target detection method

A hyperspectral image and target detection technology, which is applied in the field of hyperspectral image salient target detection based on matrix decomposition, can solve the problem of inhomogeneity of salient objects, and achieve the effect of avoiding uneven block and eliminating adverse effects

Active Publication Date: 2017-11-03
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

The traditional method of using the idea of ​​​​region comparison leads to the problem of inhomogeneity inside the salient object, while the method of the present invention is based on matrix decomposition, jumping out of the framework of the traditional method to make the interior of the salient object homogeneous

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  • Matrix factorization-based hyperspectral image saliency target detection method
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Embodiment Construction

[0012] The present invention will be further described below in conjunction with the examples, and the present invention includes but not limited to the following examples.

[0013] The hyperspectral remote sensing image has a cubic structure. The spatial dimension reflects the reflectance of pixels corresponding to different positions on the ground in a certain sunlight band, and the spectral dimension reflects the relationship between incident light and reflected light in different bands of pixels at a certain position. A hyperspectral image can be expressed as a p×n data set Y n ={y 1 ,y 2 ,...,y n}, where y i is the original spectral vector corresponding to pixel i, i=1,2,...,n, n is the total number of pixels in the hyperspectral image.

[0014] 1. Spectral gradient feature generation

[0015] The spectral gradient refers to the ratio of the difference between every two adjacent components along the original spectral vector to the difference of the corresponding wave...

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Abstract

The invention provides a matrix factorization-based hyperspectral image saliency target detection method. According to the method, the spectral gradients of an original hyperspectral image are calculated in spectral dimension, the spectral gradient features of the image are extracted, and therefore, adverse effects caused by illumination are eliminated, and at the same time, an image feature matrix is constructed; matrix low-rank sparse decomposition is performed on the image feature matrix, so that a low-rank matrix corresponding to a background part and a sparse matrix corresponding to a saliency target are obtained; and therefore, the problem of nonuniformity of block division in a salient object can be solved, and saliency target detection is realized with decreased computational complexity.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a hyperspectral image salient target detection method, in particular to a hyperspectral image salient target detection method based on matrix decomposition. Background technique [0002] Hyperspectral image is the image data obtained by recording the spectral information of various ground objects observed in the field of view by using an imaging spectrometer. With the maturity of hyperspectral imaging technology, imaging equipment has greatly improved its spectral resolution and spatial resolution. As a result, subjects such as object detection, recognition, and tracking, which were originally carried out on conventional images, can gradually be extended to hyperspectral data. At present, the relevant research on the problem of salient object detection in hyperspectral images is still in the development stage. The existing hyperspectral image salient target detection met...

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Application Information

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IPC IPC(8): G06T7/11G06K9/32G06K9/46
CPCG06T7/40G06T7/45G06T2207/10036
Inventor 魏巍张磊高一凡严杭琦张艳宁
Owner NORTHWESTERN POLYTECHNICAL UNIV
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