基于核PCA方法的高分辨率遥感图像自动解译

doi:10.6046/gtzyyg.2011.03.15

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摘要

针对基于像元的高分辨率遥感图像自动解译存在的缺点,提出一种分三步走的高分辨率遥感图像自动解译技术流程: 首先采用核PCA进行特征提取,然后采用支持向量机(Support Vector Machine,SVM)进行分类,最后采用择多滤波器进行分类后处理。通过对覆盖西藏山南地区的IKONOS图像的解译实验表明,本文方法能够有效地实现遥感图像自动解译,其结果与人工目视解译图基本一致,取得了理想的效果。

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关键词 ：简单多边形, 凹凸点判断, 向量积法

Abstract：

To tackle the limitation of conventional pixel-based classification methods, this paper proposes a new approach composed of three steps, namely kernel principal component analysis (KPCA) based feature extraction, support vector machine (SVM) classification and majority filtering post-classification. An experiment with an IKONOS image covering a study area in Tibet indicates the effectiveness of this approach. The resultant image from this automatic method shows a pattern very similar to the pattern of the reference map interpreted manually.

Key words： Simple polygon Identifying convexity-concavity Vector-product method

收稿日期: 2010-12-16 出版日期: 2011-09-07

TP 751.1

基金资助:

中国地质调查局地质调查项目"青藏铁路沿线矿产资源遥感调查"(编号: 1212010781043)资助。

作者简介: 张微(1980-),男,蒙古族,博士,高级工程师,主要从事遥感技术在矿产资源调查及新构造运动方面的研究,已发表论文50余篇。

引用本文:

张微, 张伟, 刘世英, 杨金中, 茅晟懿. 基于核PCA方法的高分辨率遥感图像自动解译[J]. 国土资源遥感, 2011, 23(3): 82-87.
ZHANG Wei, ZHANG Wei, LIU Shi-ying, YANG Jin-zhong, MAO Sheng-yi. Automatic Interpretation of High Resolution Remotely Sensed Images by Using Kernel Method. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(3): 82-87.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2011.03.15 或 https://www.gtzyyg.com/CN/Y2011/V23/I3/82

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