Spatial Neighboring Clustering Method for Hyperspectral Imagery Based on Kernel Spectral Angel Cosine

CHEN De-rong; SUN Bo; TAO Peng; GONG Jiu-lu

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Spatial Neighboring Clustering Method for Hyperspectral Imagery Based on Kernel Spectral Angel Cosine

更新时间：2025-07-16

- Spatial Neighboring Clustering Method for Hyperspectral Imagery Based on Kernel Spectral Angel Cosine
- Acta Electronica Sinica Vol. 36, Issue 10, Pages: 1992-1995(2008)
- 作者机构：
  
  1. 北京理工大学宇航科学技术学院,北京,100081
  2. 北京师范大学信息科学与技术学院,北京,100875
  3. 北京理工大学宇航科学技术学院北京,100081
  4. 北京师范大学信息科学与技术学院北京,100875
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP751
- Published：2008
- 稿件说明：
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CHEN De-rong, SUN Bo, TAO Peng, et al. Spatial Neighboring Clustering Method for Hyperspectral Imagery Based on Kernel Spectral Angel Cosine[J]. Acta Electronica Sinica, 2008, 36(10): 1992-1995.
DOI：

CHEN De-rong, SUN Bo, TAO Peng, et al. Spatial Neighboring Clustering Method for Hyperspectral Imagery Based on Kernel Spectral Angel Cosine[J]. Acta Electronica Sinica, 2008, 36(10): 1992-1995. DOI：

摘要

针对高光谱图像分类精度对光谱角余弦阈值非常敏感的问题

提出了一种新的光谱相似性度量方法

即核光谱角余弦.论文给出了核光谱角余弦的定义及求解式;分析了多项式核函数参数对核光谱角余弦影响

设计了以核光谱角余弦作为相似性度量的空间邻域聚类方法.对深圳红树林高光谱图像的仿真结果表明

基于核光谱角余弦完成空间邻域聚类时

阈值取值区间大小约为现有基于光谱角余弦的阈值取值区间大小的9倍.

Abstract

The paper presented the concept of KSAC (Kernel Spectral Angel Cosine) to address the restrictions that the classification precision of hyperspectral imagery is very sensitive to segmentation threshold based on SAC (Spectral Angel Cosine).First

we defined the representation of KSAC

and then analyzed the effect of polynomial kernel-function parameter on KSAC

at last

we presented the method of spatial neighboring clustering based on KSAC.The experiments on the hyperspectral imagery of Shenzhen Red-forest field indicate that the threshold area coverage of the spatial neighboring clustering based on KSAC is extended up to nine times of that based on SAC.

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