Hyperspectral image space spectrum classification method and device considering spectral importance

A classification method and an important technology, applied in the field of hyperspectral remote sensing image processing, can solve the problems of lack of learning mechanism for sensitive spectral information of ground objects, suppress classification noise, maintain local detail information, etc., and achieve high-dimensional data processing capabilities and noise robustness Strong stickiness, improved classification effect, and improved distinguishability effect

CN110796163AActive Publication Date: 2020-02-14CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-02-14

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Abstract

The invention discloses a hyperspectral image space spectrum classification method and device considering spectral importance, and the method comprises the steps: calculating the spectral feature importance of a given hyperspectral remote sensing image with hundreds of spectral bands through a random forest; customizing a spectral weight characteristic kernel function, modeling the relative effectof each spectral band in classification by utilizing the extracted spectral characteristic weight, giving a larger weight to the band which is more beneficial to ground object category identification, and improving the spectral discrimination capability of the ground object; constructing spectral unitary potential energy and spatial binary potential energy under a unified framework of a conditional random field, considering spectral characteristic weights in the spectral unitary potential energy, integrating a spectral weight kernel function to improve the distinguishing capability of different spectral characteristics in a nonlinear space, and modeling the spatial correlation of ground objects through the spatial binary potential energy. The beneficial effects of the method are that themethod reduces the impact on classification from an unimportant waveband, improves the discrimination of types, and improves the classification effect.
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Description

technical field

[0001] The invention belongs to the field of hyperspectral remote sensing image processing, and in particular relates to a hyperspectral image spatial spectrum classification method and device taking into account the importance of the spectrum. Background technique

[0002] Hyperspectral remote sensing images have the unique advantages of high spectral resolution and map-spectrum integration, which can provide diagnostic spectral features of different ground objects, and are an important data source for classification and recognition of ground objects. In recent years, with the rapid development of hyperspectral technology, hyperspectral remote sensing images with high spatial resolution (double high remote sensing images) have begun to emerge, such as the domestic Tiangong-1 satellite, aviation ROSIS, CASI, and drones. The spatial resolution of hyperspectral imagery has reached meter level, or even submeter level. This kind of hyperspectral image with high ...

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Embodiment Construction

[0047] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the accompanying drawings.

[0048] refer to figure 1 , which is a flow chart of the hyperspectral image spatial spectral classification method in consideration of spectral importance provided by the present invention. According to the core points realized by the hyperspectral image spatial spectral classification method of the present invention, the main implementation of the present invention is divided into Follow the steps below:

[0049] Step 1: Spectral Band Importance Extraction

[0050] Step 2: Custom Spectral Weight Kernel

[0051] Step 3: Conditional Random Field Framework Construction

[0052] The specific implementation of step 1 includes the following sub-steps,

[0053] In step 1.1, the random forest algorithm is used to judge the...