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Semi-supervised hyperspectral sub-pixel target detection method based on enhanced constraint sparse regression method

A technology of target detection and sparse regression, applied in the field of hyperspectral remote sensing detection, to achieve the effect of improving the detection rate, simplifying the solution process, and reducing the false alarm rate

Active Publication Date: 2014-03-19
NANJING UNIV OF SCI & TECH
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Problems solved by technology

[0005] The purpose of the present invention is to provide a semi-supervised sub-pixel target detection method for hyperspectral remote sensing images, combined with hyperspectral unmixing and generalized likelihood ratio testing methods, to solve the problem of detection and recognition of specific targets in remote sensing information processing

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  • Semi-supervised hyperspectral sub-pixel target detection method based on enhanced constraint sparse regression method
  • Semi-supervised hyperspectral sub-pixel target detection method based on enhanced constraint sparse regression method
  • Semi-supervised hyperspectral sub-pixel target detection method based on enhanced constraint sparse regression method

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Embodiment Construction

[0018] The present invention is a technology for target detection and recognition using hyperspectral images. Based on the idea of ​​combining enhanced constrained sparse regression and generalized likelihood ratio testing, specific target detection and recognition is performed at the sub-pixel level of hyperspectral images. The invention belongs to the field of remote sensing image processing and has application prospects in the fields of environment detection, military reconnaissance, geological exploration, disaster early warning and the like.

[0019] The present invention is based on the enhanced constrained sparse regression semi-supervised hyperspectral sub-pixel target detection method, the specific scheme is as follows figure 1 shown, including the following steps:

[0020] Step 1. Use the ground hyperspectral imager to collect hyperspectral signals of typical ground objects, and collect reflectance spectral curves of various ground objects to construct a spectral li...

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Abstract

The invention discloses a semi-supervised hyperspectral sub-pixel target detection method based on an enhanced constraint sparse regression method. According to the enhanced constraint sparse regression method, the precision and the stability for decomposing a hyperspectral mixed-pixel are improved; a statistical model for distinguishing a target pixel and a background pixel is constructed by applying a generalized likelihood radio checking theory; and furthermore, the enhanced constraint sparse regression method and the generalized likelihood radio checking theory are combined, so that the target distribution can be quantitatively detected, and the detection efficiency can be improved effectively, and the mis-alarm probability is reduced.

Description

Technical field [0001] The present invention involves high spectrometer remote sensing detection technology fields, especially a target detection method based on enhanced restrictions on sparse and semi -supervised high spectrometer Asians. Background technique [0002] High spectrum data has a high spectral resolution, and can obtain many very narrow spectral waves in the range of visible light, near infrared, medium infrared and hot infrared bands of electromagnetic spectrum, so as to obtain high -dimensional spectral data, which is widely used in widely used inEnvironmental monitoring, agricultural production, military reconnaissance, geological exploration, land and resources utilization, disaster warning, urban planning, etc. are related to the key science and technology fields of national economy and people's livelihood.Each of high -spectrum remote sensing images can reach dozens of or hundreds of each like meta, compared with low -dimensional full -colored images, color i...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01V8/02G01C11/00G06K9/00
Inventor 宋义刚吴泽彬韦志辉孙乐刘建军
Owner NANJING UNIV OF SCI & TECH
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