Hyper-spectral mixed pixel decomposition method based on geometric spatial spectral structure information

A hybrid pixel decomposition and structure information technology, which is applied in the field of hyperspectral data unmixing and hyperspectral hybrid pixel decomposition based on geometric spatial spectral structure information, can solve the problem of not taking into account the similar characteristics of adjacent similarity and the inconsistency of distribution ratio calculation. Accuracy, inaccurate identification of end members, etc.

Active Publication Date: 2015-02-04
XIDIAN UNIV
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Problems solved by technology

However, these methods have the problems of inaccurate identification of endmembers and inaccurate calculation of distribution ratios for remote sensing data without pure endmembers, and do not

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  • Hyper-spectral mixed pixel decomposition method based on geometric spatial spectral structure information
  • Hyper-spectral mixed pixel decomposition method based on geometric spatial spectral structure information
  • Hyper-spectral mixed pixel decomposition method based on geometric spatial spectral structure information

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Embodiment Construction

[0047] refer to figure 1 , the concrete implementation of the present invention is as follows:

[0048] Step 1. Preprocess the hyperspectral raw hybrid data.

[0049] Select the hyperspectral data I whose scene size is a×b pixels, the number of bands is S, the number of pixels is N, and N=a×b. Since the real hyperspectral data has a large value, it needs to be normalized on the spectral dimension to facilitate subsequent calculations. The preprocessing process not only includes normalization processing, but also needs to filter out noisy bands to obtain new hyperspectral data

[0050] Specific steps are as follows:

[0051] 1a) Input raw hyperspectral image data S is the number of original bands;

[0052] 1b) Arranging three-dimensional data I into two-dimensional data

[0053] 1c) Extract the bands that are greatly affected by noise on the remote sensing data, and obtain the spectral data after band selection

[0054] 1d) Normalize the data matrix Z according to t...

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Abstract

The invention belongs to the technical field of remote sensing data processing, and particularly discloses a mixed pixel decomposition method based on geometric spatial spectral structure information, so as to solve the problems that classifications of hyper-spectral image mixed pixel point surface features are not distinct and distribution is not accurate. The method comprises steps: 1) hyper-spectral data are inputted, and the data are arranged in a matrix after pretreatment; 2) a VD method is used for estimating the number of pure end members; 3) an edge contour of the image is extracted; 4) a formula for computing a spatial distance is brought forward according to the edge and the position; 5) a formula for computing a spectral distance is brought forward according to spectral statistic information; 6) a geometric spatial spectral binding term is built according to the spatial distance and the spectral distance, and the binding term is added to an NMF model; and 7) an output end member matrix and an abundance matrix are unmixed in new NMF algorithm, and the scene surface feature classifications and the distribution ratio are judged. The method is well applicable to different hyper-spectral data, and compared with the prior method, the precision of mixed pixel decomposition is improved, and great value is provided for target detection and recognition.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral remote sensing data processing, and relates to hyperspectral data unmixing, in particular to a hyperspectral mixed pixel decomposition method based on geometric spatial spectral structure information, which can be used for ground object detection, target anomaly detection and sub-pixel identification Research. Background technique [0002] Hyperspectral remote sensing technology is a new remote sensing technology developed in the past ten years. Compared with traditional multispectral data, hyperspectral remote sensing data has richer spectral information, and its wide application shows the great potential of hyperspectral remote sensing technology. For its military, industrial and civilian applications, the corresponding processing techniques include: dimensionality reduction, target detection, change detection, endmember extraction, mixed pixel decomposition and classification. Although ...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/10036
Inventor 杨淑媛焦李成程时倩刘芳侯彪刘红英熊涛任宇冯志玺任永恒
Owner XIDIAN UNIV
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