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Sparse graph coding-based hyperspectral remote sensing image eigen decomposition method and system

A hyperspectral remote sensing and sparse map technology, which is applied in the field of hyperspectral remote sensing image eigendecomposition method and system, can solve the problems of inability to effectively maintain the boundaries of ground objects and low precision, so as to maintain the boundaries of ground objects, improve accuracy, and avoid aliasing effect

Active Publication Date: 2021-06-15
HARBIN INST OF TECH
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  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the current eigendecomposition method cannot effectively maintain the boundaries of ground objects when applied to hyperspectral images, which in turn leads to the problem of low accuracy in generating reflectance components of hyperspectral images, and proposes a sparse graph coding based Hyperspectral remote sensing image eigendecomposition method and system

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  • Sparse graph coding-based hyperspectral remote sensing image eigen decomposition method and system
  • Sparse graph coding-based hyperspectral remote sensing image eigen decomposition method and system
  • Sparse graph coding-based hyperspectral remote sensing image eigen decomposition method and system

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specific Embodiment approach 1

[0021] Specific implementation mode 1: This implementation mode is based on the hyperspectral remote sensing image eigendecomposition method based on sparse graph coding, and the specific process is as follows:

[0022] Step 1. Constructing a sparse graph coding dictionary for hyperspectral remote sensing images, including the following steps:

[0023] Step 11. Obtain hyperspectral remote sensing images:

[0024]

[0025] Among them, H=[H 1 ,H 2 ,...,H n ]∈R d×n , H is the hyperspectral remote sensing image, n is the total number of pixels on the hyperspectral remote sensing image, d represents the spectral dimension of the hyperspectral remote sensing image H, R d×n is a collection of matrices of size d×n on the real number field, ρ is the reflectivity component, 1 n is an n×1 all-one-column vector, is a 1×n full 1-row vector, 1 d is a full 1-column vector of d×1, β=[β 1 ,β 2 ,...,β n ] T is the column vector of the n×1 direction matrix, β T is the row vector ...

specific Embodiment approach 2

[0053]Specific Embodiment 2: The hyperspectral remote sensing image eigendecomposition system based on sparse graph coding is used to realize the hyperspectral remote sensing image eigendecomposition method based on sparse graph coding. The system includes: acquisition module, construction module, calculation module, decomposition module( image 3 );

[0054] The acquisition module is used to acquire hyperspectral remote sensing images;

[0055] The construction module is used to construct a sparse graph coding dictionary of a hyperspectral remote sensing image;

[0056] The calculation module is used to solve the similarity matrix of the sparse graph;

[0057] The decomposition module is used to decompose and obtain the reflectance components of the hyperspectral remote sensing image.

specific Embodiment approach 3

[0058] Specific embodiment three: the acquisition module is used to acquire hyperspectral remote sensing images, including the following steps:

[0059]

[0060] Among them, H=[H 1 ,H 2 ,...,H n ]∈R d×n , H is the hyperspectral remote sensing image, n is the total number of pixels on the hyperspectral remote sensing image, d represents the spectral dimension of the hyperspectral remote sensing image H, R d×n is a collection of matrices of size d×n on the real number field, ρ is the reflectivity component, 1 n is an n×1 all-one-column vector, is a 1×n full 1-row vector, 1 d is a full 1-column vector of d×1, β=[β 1 ,β 2 ,...,β n ] T is the column vector of the n×1 direction matrix, β T is the row vector of the n×1 direction matrix, E is the ambient light, E=[E(λ 1 ), E(λ 2 ),…,E(λ d )] T ∈ R d×1 , λ represents the wavelength of light.

[0061] Other steps are the same as in the second embodiment.

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Abstract

The invention discloses a sparse graph coding-based hyperspectral remote sensing image eigen decomposition method and system, and relates to the field of image processing. According to the method, the problem that the precision of generating the reflectivity component of the hyperspectral image is low due to the fact that the surface feature boundary cannot be effectively kept when an existing intrinsic decomposition method is applied to the hyperspectral image is solved. The method comprises the following steps: acquiring a hyperspectral remote sensing image; averaging a hyperspectral remote sensing image geometrically in a spectral dimension to remove spectral change caused by geometric distribution on the surface of an object, so that the image is geometrically averaged in a spatial dimension; eliminating spectral change caused by illumination changing along with spatial distribution to obtain an image, and obtaining a sparse graph coding dictionary of each pixel in the hyperspectral remote sensing image; obtaining a similarity matrix of a sparse graph according to the sparse graph coding dictionary of the hyperspectral remote sensing image; obtaining a reflectivity component of the hyperspectral image according to the sparse image similarity matrix; the system comprises an acquisition module, a construction module, a calculation module and a decomposition module.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a hyperspectral remote sensing image eigendecomposition method and system based on sparse graph coding. Background technique [0002] In recent years, remote sensing imaging technology has continued to develop. Satellite systems carrying hyperspectral sensors can collect surface reflectance data from different wavelengths. The hyperspectral images obtained contain rich spectral and spatial information, which is of great significance for the accurate classification of different ground objects. . In order to make full use of the spectral and spatial information of hyperspectral images, it is necessary to extract features effectively, which is also an important research topic of hyperspectral image classification in the past two decades. Intrinsic decomposition refers to removing the influence of light or shadow from the original data to achieve the purpose of restoring the reflecta...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T9/00G06T5/00G06K9/00G06K9/62
CPCG06T9/00G06T2207/10032G06V20/13G06F18/2136G06F18/28G06F18/22G06T5/90
Inventor 谷延锋谢雯金旭东
Owner HARBIN INST OF TECH
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