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具有更佳分辨率小波分解的遥感影像纹理分类*
引用本文:朱长青 杨晓梅. 具有更佳分辨率小波分解的遥感影像纹理分类*[J]. 地理研究, 1997, 16(1): 53-59. DOI: 10.11821/yj1997010007
作者姓名:朱长青 杨晓梅
作者单位: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
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  
收稿时间:1996-08-19
修稿时间:1996-10-29

REMOTE SENSINGIMAGE TEXTURE CLASSIFICATION BASED ON BEST-RESOLUTION WAVELET FEATURES
Zhu Changqing. REMOTE SENSINGIMAGE TEXTURE CLASSIFICATION BASED ON BEST-RESOLUTION WAVELET FEATURES[J]. Geographical Research, 1997, 16(1): 53-59. DOI: 10.11821/yj1997010007
Authors:Zhu Changqing
Affiliation: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
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 tex- ture 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 resolu- tions of the image. Each sub-section 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 infor- mation 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 condi-tions of different features , different filters and different resolutions , and achieved highly accurate classificatory results.
Keywords:the wavelet transform  best resolution  remote sensing image  texture classification
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