地理研究

地理研究 ›› 1997, Vol. 16 ›› Issue (1): 53-59.doi: 10.11821/yj1997010007

• 论文 • 上一篇    下一篇

具有更佳分辨率小波分解的遥感影像纹理分类*

朱长青1,2, 杨晓梅3   

  1. 1. 浙江大学CAD&CG国家实验室, 310027;
    2. 郑州解放军测绘学院, 450052;
    3. 中国科学院、国家计划委员会地理研究所, 北京100101
  • 收稿日期:1996-08-19 修回日期:1996-10-29 出版日期:1997-03-15 发布日期:1997-03-15
  • 基金资助:
    * 国家自然科学基金资助项目(49571060)

REMOTE SENSINGIMAGE TEXTURE CLASSIFICATION BASED ON BEST-RESOLUTION WAVELET FEATURES

Zhu Changqing1,2, Yang Xiaomei3   

  1. 1. Zhengzhou Institute of Surveying and Mapping, Zhengzhou 450052;
    2. State Key Lab.of CAD&CG, Zhejiang University, Hangzhou 310027;
    3. Institute of Geography, Chinese Academy of Sciences, Beijing 100101
  • Received:1996-08-19 Revised:1996-10-29 Online:1997-03-15 Published:1997-03-15

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摘要： 首先提出了具有更佳分辨率的小波分解,然后研究了基于该小波分解特征的影像纹理分类,并对25类地貌遥感影像在两种不同分解方式、两种不同滤波器长度及三种不同分辨率下进行了分类试验,取得了较高的分类正确率。

关键词: 小波变换, 更佳分辨率, 遥感影像, 纹理分类

Abstract: The wavelet transform is an applied mathematical theory which rose in the middle 1980s. And it has been applied widely in many fields such as image processing. The authors have been studying the features of cross wavelet transform of images. And some good results have been obtained in texture classification of geomorphologic image. In this paper, a kind of wavelet features with best-resolution was put out. We know, the cross wavelet transform is to resolve the image at varied levels of a framework, that is different resolutions of the image. Each subsection of the framework has a unique feature of frequency and spatial orientation. However, most of the important information of the texture image is located in the range of medium frequency. So if the cross wavelet transform is resolved in this range, more texture information will be obtained. This is a kind of wavelet features with best-resolution. In the view of the above, the classification experiment was carried out for 25 geomorphologic images under the conditions of different features, different filters and different resolutions, and achieved highly accurate classificatory results.

Key words: the wavelet transform, best resolution, remote sensing image, texture classification

PACS: 

  • TP751.1