A hyperspectral remote sensing image recognition method and system based on superpixels and a medium

By combining spectral dimensionality reduction and superpixel segmentation with structural feature extraction, the problems of missegmentation and inconsistent ground object boundaries in hyperspectral remote sensing image classification were solved, achieving higher classification accuracy and visual effects.

CN119068342BActive Publication Date: 2026-05-29HUNAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUNAN UNIV
Filing Date
2024-09-12
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing hyperspectral remote sensing image classification methods based on superpixels are prone to missegmentation at complex edges or structures, leading to reduced classification accuracy. Furthermore, feature extraction-based methods fail to effectively consider ground feature boundary information, resulting in inconsistent classification results.

Method used

This method combines spectral dimensionality reduction, superpixel segmentation, structural feature extraction, and a multi-class support vector machine model. It obtains superpixel-level class probabilities through spectral dimensionality reduction, optimizes spatial relationships using an extended random walk method, extracts structural features using a weighted least squares filtering algorithm, and finally performs probability fusion to obtain the final classification result.

Benefits of technology

It improves the classification accuracy and visual effect of hyperspectral remote sensing images, effectively integrates superpixel and structural feature information, optimizes the problem of ground object misclassification, and performs particularly well in small sample cases.

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Abstract

The present application relates to hyperspectral remote sensing image processing technical field, specifically to a kind of hyperspectral remote sensing image recognition method, system and medium based on superpixel, wherein hyperspectral remote sensing image recognition method includes the following steps: 1, the source hyperspectral image is carried out spectral dimension reduction, obtain the hyperspectral image after dimension reduction;2, extract superpixel information from source hyperspectral image, and obtain superpixel level class probability, then the superpixel level class probability is spatially optimized, and the optimization probability of different ground objects is obtained;3, extract structural features from the hyperspectral image after dimension reduction, and obtain feature level class probability;4, the optimization probability of different ground objects and feature level class probability are fused, and the final classification result is obtained.The present application can fully and accurately mine spatial information in superpixel, and fuse edge information to optimize ground object misclassification problem, and simultaneously, the present application can obtain higher ground object classification precision and better visual effect.
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