Compact-dictionary sparse-representation-based hyperspectral-remote-sensing-image classification method

A technology of hyperspectral remote sensing and classification method, which is applied in the field of hyperspectral remote sensing image classification based on sparse representation of compact dictionary, and can solve the problems of ignoring spatial information and so on.

Active Publication Date: 2018-06-29
XIANGTAN UNIV
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Problems solved by technology

However, these methods ignore another more explicit spatial information, that is, the category reference provided by known labels.

Method used

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  • Compact-dictionary sparse-representation-based hyperspectral-remote-sensing-image classification method

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Embodiment Construction

[0056] The present invention will be further described below in conjunction with accompanying drawing.

[0057] (1) Read in a hyperspectral remote sensing image I with a size of L×W×B, where L is the vertical length of the hyperspectral remote sensing image, W is the horizontal width, and B is the number of spectral bands included;

[0058] (2) Read in the training sample marker matrix F of size L×W, and the element F(i,j) in F corresponds to the spectral vector s at the corresponding position in I i,j =I(i,j,:), and

[0059]

[0060] Where L is the number of rows of the matrix, W represents the number of columns of the matrix, F(i,j) represents the element in the i-th row and j-th column in the matrix F, and I(i,j,:) represents all the bands in I located in A column vector composed of elements in the i-th row and the j-th column, and satisfying 1≤i≤L, 1≤j≤W, c represents the category label corresponding to the training sample, and its value is an integer between 1 and C, ...

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Abstract

The invention discloses a compact-dictionary sparse-representation-based hyperspectral-remote-sensing-image classification method, which effectively improves class interference solving time-consumingproblems caused by using fixed full-class dictionaries by existing residual-based sparse-representation classification method. In a classification process of the method, neighborhood label informationand a spectral similarity measurement method are utilized to construct a compact dictionary of self-adaptive classes for each test sample, limited local label information is transmitted to a wider area in a hyperspectral remote-sensing image through a space position expansion strategy, thus space information of the hyperspectral remote-sensing image is more fully explored, a scale of the dictionary and a classification decision range are reduced at the same time, solving time of sparse coefficients is enabled to be greatly decreased, and a speed and an accuracy rate of classification are significantly increased. The method can be used in the fields of agriculture, environment monitoring, military defense and the like.

Description

technical field [0001] The invention belongs to the field of image processing and relates to hyperspectral remote sensing image processing and sparse representation classification, in particular to a hyperspectral remote sensing image classification method based on compact dictionary sparse representation. technical background [0002] Hyperspectral remote sensing image is a kind of data collected by hyperspectral sensors mounted on satellites or UAVs. It consists of hundreds to thousands of almost continuous bands and contains rich spectral information. A large number of spectral band information can fully reflect the potential differences of different ground features, so hyperspectral remote sensing images are widely used in various fields, such as agriculture, military defense, environmental monitoring, etc. Hyperspectral remote sensing image classification, as a basic part in many application fields, has been extensively studied and concerned by scholars at home and abro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/217G06F18/241
Inventor 曹春红邓柳段伟胡凯肖芬杨万春
Owner XIANGTAN UNIV
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