Target detection method and device based on hyperspectral data clustering analysis

By using hyperspectral data clustering analysis, the problem of target recognition accuracy under low signal-to-noise ratio conditions in navigation path planning was solved, achieving high-precision and low-cost target detection, which is suitable for unmanned platform navigation.

CN122289665APending Publication Date: 2026-06-26NAT UNIV OF DEFENSE TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NAT UNIV OF DEFENSE TECH
Filing Date
2026-05-06
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

In navigation path planning, traditional manual feature extraction methods and deep learning methods are susceptible to noise interference under low signal-to-noise ratio conditions, leading to feature extraction bias and affecting target recognition accuracy.

Method used

A clustering analysis method based on hyperspectral data is adopted. The band dimension is compressed by noise-aware principal component analysis to generate pseudo-RGB images. Contrast-guided linear iterative clustering and peak clustering of effective neighborhood density are used to calculate the significance score of the clusters to distinguish between the background and the target.

Benefits of technology

It effectively suppresses noise interference, improves target detection accuracy, reduces data processing volume, adapts to different environments, reduces deployment costs, and meets real-time navigation requirements.

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Abstract

This application relates to the field of target recognition technology, providing a target detection method and apparatus based on hyperspectral data clustering analysis. It reduces the dimensionality of hyperspectral data by compressing the band dimension through noise-aware principal component analysis, thereby identifying the main band components and reducing the amount of hyperspectral data processing. A contrast-guided linear iterative clustering method is used to perform superpixel segmentation on pseudo-RGB images, ensuring boundary sensitivity and preserving the image structural features of targets in navigation scenarios. A peak clustering method based on effective neighborhood density is used to determine the cluster centers of superpixel blocks, effectively suppressing noise contamination of the density field. Based on extended boundary connectivity scores and spatial weights, a saliency score is calculated for each cluster, distinguishing between background and target based on the score, and outputting the target detection result after background removal. This ensures detection accuracy in navigation scenarios and provides reliable support for path planning.
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