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A high spatial-high spectral resolution remote sensing image eigendecomposition method and system

A high spatial resolution, hyperspectral remote sensing technology, applied in the field of eigendecomposition of high spatial-hyperspectral resolution remote sensing images, can solve problems such as inability to decompose, achieve improved spatial resolution, avoid aliasing, and maintain ground objects The effect of borders

Active Publication Date: 2022-07-08
HARBIN INST OF TECH
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Problems solved by technology

[0006] In order to solve the problem that the existing eigendecomposition method cannot decompose the hyperspectral remote sensing image with low spatial resolution to obtain the reflectance image with high spatial resolution, a high spatial-high spectral resolution remote sensing image eigen Decomposition method and system

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  • A high spatial-high spectral resolution remote sensing image eigendecomposition method and system
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  • A high spatial-high spectral resolution remote sensing image eigendecomposition method and system

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specific Embodiment approach 1

[0072] Embodiment 1: Combining figure 1 This embodiment will be described. A method for eigendecomposition of remote sensing images with high spatial-hyperspectral resolution described in this embodiment includes the following steps:

[0073] Step 1: Obtain panchromatic remote sensing images and hyperspectral remote sensing images, analyze the spatial information of the panchromatic remote sensing images and the spectral information of the hyperspectral remote sensing images, use the spatial information of the panchromatic remote sensing images to construct the spatial structure consistency constraint, and use the hyperspectral remote sensing images. The spectral information of the remote sensing image constructs the spectral consistency constraint, which fully considers the spectral characteristics of the hyperspectral remote sensing image and the spatial characteristics of the panchromatic remote sensing image, and goes to step 2;

[0074] Step 2: Calculate the similarity ma...

specific Embodiment approach 2

[0076] Specific implementation mode 2: Combining figure 1 This embodiment will be described. In a method for eigendecomposition of remote sensing images with high spatial-hyperspectral resolution described in this embodiment, step 1 includes:

[0077] Step 11: Obtain panchromatic remote sensing images and hyperspectral remote sensing images, and analyze the spatial information of the panchromatic remote sensing images and the spectral information of the hyperspectral remote sensing images;

[0078] Step 12: According to the spatial information of the panchromatic remote sensing image and the spectral information of the hyperspectral remote sensing image obtained in the step 11, respectively calculate the spatial information consistency constraint and the spectral information consistency constraint;

[0079] The high spatial resolution hyperspectral remote sensing image (HR-HSI) can be decomposed into two intrinsic components, namely the light and dark components and the reflec...

specific Embodiment approach 3

[0106] Specific implementation three: combination figure 1 This embodiment is described. In the method for eigendecomposition of remote sensing images with high spatial-hyperspectral resolution described in this embodiment, step 2 includes:

[0107] For the reflectance component, the same material has the same reflectance, so the reflectance component has a strong correlation in the local neighborhood, and its linear relationship is expressed as follows:

[0108]

[0109] Among them, ω i Represents a local window of size (2r+1)×(2r+1) centered on pixel i;

[0110] W(i,j) represents the similarity between pixel i and pixel j, W∈R N×N ;

[0111] represents the logarithmic domain reflectance component of the ith pixel;

[0112] represents the logarithmic domain reflectance component of the jth pixel;

[0113] Based on the optic nerve (Retinex) theory, the reflectivity of object materials generally only changes greatly at the edge, and the reflectivity can be regarded ...

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Abstract

A high spatial-hyperspectral resolution remote sensing image eigendecomposition method and system, in particular to a hyperspectral remote sensing image eigendecomposition method and system integrating high spatial resolution information, and a computer-readable storage medium for storing the same , the method includes: first, obtaining the spatial structure consistency constraints of panchromatic remote sensing images and spectral consistency constraints of hyperspectral remote sensing images; second, obtaining the reflectance component consistency constraints; third, obtaining the reflectance components. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of any one of the methods. The invention is used for intrinsically decomposing hyperspectral remote sensing images with low spatial resolution to obtain reflectivity images with high spatial resolution, and belongs to the field of remote sensing image processing.

Description

technical field [0001] The present invention relates to a method and system for eigendecomposition of high spatial-hyperspectral resolution remote sensing images and a computer-readable storage medium for storing the same, in particular to an eigendecomposition of hyperspectral remote sensing images fused with high spatial resolution information The method, the system and the computer-readable storage medium for storing the same belong to the field of remote sensing image processing. Background technique [0002] In recent years, with the continuous development of remote sensing imaging technology, more and more remote sensing satellites carrying hyperspectral sensors have been launched to perform the task of collecting surface reflectance data of a wide range of wavelengths. Therefore, hyperspectral remote sensing images contain Rich spectral information, which is of great significance for applications such as accurate classification of ground objects, target recognition, a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/13G06K9/62G06V10/74G06V10/764G06V10/58
CPCG06F18/22G06F18/24Y02A40/10
Inventor 谷延锋谢雯金旭东
Owner HARBIN INST OF TECH
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