Adaptive Unmixing Method for Hyperspectral Image Based on Region Segmentation

A hyperspectral image and region segmentation technology, which is applied in the field of image processing and can solve problems such as inapplicability to hyperspectral data

Active Publication Date: 2017-10-24
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] In order to overcome that the existing hyperspectral image unmixing method is not suitable for dealing with hyperspectral data where linear mixture and bilinear mixture models coexist, the present invention provides a hyperspectral segmentation based on region segmentation considering the coexistence of linear mixture and bilinear mixture Image Adaptive Unmixing Method

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  • Adaptive Unmixing Method for Hyperspectral Image Based on Region Segmentation
  • Adaptive Unmixing Method for Hyperspectral Image Based on Region Segmentation

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Embodiment 1

[0048] The present invention is a hyperspectral image adaptive unmixing method based on region segmentation, referring to figure 1 , the specific implementation steps of the present invention include:

[0049] (1) Input hyperspectral image, hyperspectral image data Y∈R L×N where L represents the number of bands of hyperspectral data, N represents the total number of hyperspectral data samples, and R represents the real number domain; in this example, the rgb image of the 30th band of the input hyperspectral image is as follows figure 2 As shown, the figure contains three substances: vegetation, water and soil. Hyperspectral unmixing is to obtain the percentages of these three different substances, that is, the abundance map.

[0050] (2) Estimate hyperspectral data Y∈R using the minimum error hyperspectral signal recognition method L×N The signal subspace of the signal subspace, the dimension K of the signal subspace is obtained, that is, the number of endmembers of the hy...

Embodiment 2

[0060] The hyperspectral image adaptive unmixing method based on region segmentation is the same as embodiment 1, wherein step (5) described with L 1 / 2 Constrained non-negative matrix factorization method to obtain homogeneous regional data Y 1 The first-order abundance matrix X 1 , including the following steps:

[0061](5a) According to the hyperspectral imaging theory, the abundance matrix X of the data in the homogeneous region of the hyperspectral image 1 Add L to 1 / 2 Norm, get the sparse constraint expression as the abundance matrix X 1 The sparse constraint term, where x 1n (k) is the hyperspectral image homogeneous region data Y 1 The abundance of the kth endmember of the nth pixel in .

[0062] (5b) Add the sparse constraint item obtained in step (5a) to the objective function of the non-negative matrix factorization algorithm based on Euclidean distance , forming a new objective function:

[0063]

[0064] Condition: X 1 ≥0,1 T x 1 = 1 T

[0065] w...

Embodiment 3

[0072] The hyperspectral image adaptive unmixing method based on region segmentation is the same as embodiment 1-2, wherein step (6) uses L 1 / 2 -Semi_NMF method to get detail area data Y 2 The corresponding first-order abundance matrix X 2 and the second-order abundance matrix E, according to the following steps:

[0073] (6a) In hyperspectral image detail area Y 2 The bilinear model is used to express as follows

[0074] Y 2 =AX 2 +BE+M

[0075] in, Represents the first-order abundance matrix corresponding to the data in the bilinear region, where each column vector represents the abundance vector of the nth pixel, is a bilinear endmember matrix, Is the second-order abundance matrix corresponding to the data in the bilinear region, where each column vector represents the bilinear abundance vector of the nth pixel, Represents the noise matrix;

[0076] (6b) The abundance matrix X of the data in the homogeneous region of the hyperspectral image 2 Add L to 1 / ...

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Abstract

The present invention discloses a method of adapting to the high spectrometer image based on regional segmentation. Considering the situation of linear hybrid and dual -linear hybrid coexistence, in fact, input high spectral images;Number of end element; use a vertex composition analysis algorithm to extract the end metatistics matrix; use the K average cluster method to high -spectrum data clustering, divide the image into a uniform area and detail area;The negative matrix decomposition method is mixed. The detail area adopts a broad -line dual -linear model.The present invention combines the characteristics of high spectrometer data spectrum and abundance to obtain a more accurate representation of high spectrometry images, which improves the accuracy of unclean mixing.Adding sparse constraints to the abundance, overcoming the disadvantages of the semi -non -negative matrix decomposition algorithm that is easy to fall into the local minimum value, and obtain more accurate abundance to identify the ground objects used for high spectrum images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to an unsupervised target recognition method, in particular to a hyperspectral image self-adaptive unmixing method based on region segmentation, which can be applied to ground object recognition of hyperspectral images. Background technique [0002] Hyperspectral remote sensing uses the principle of spectroscopy, that is, in the ultraviolet, visible, near-infrared and mid-infrared regions of the electromagnetic spectrum, many very narrow and spectrally continuous image data are obtained by imaging spectrometers. Imaging spectrometers acquire ground reflection or emission spectral signals in units of pixels. The object spatial region corresponding to each pixel in the image often contains different substances with different spectral characteristics. If the pixel contains only one substance or the proportion of the substance is very high, it is called a pure pixel, also...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06K9/62
CPCG06T5/20G06T2207/10036G06T2207/10032G06T2207/10084G06F18/232G06F18/2133
Inventor 张向荣焦李成成才李阳阳冯婕马文萍侯彪白静
Owner XIDIAN UNIV
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