Segmentation of remotely sensed images using wavelet features and their evaluation in soft computing framework

被引：35
作者：
Acharyya, M
De, RK
Kundu, MK
机构：
[1] Def Res Dev Org, E Radar Div, Elect & Radar Dev Estab, Bangalore 560093, Karnataka, India
[2] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, W Bengal, India
来源：
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2003年 / 41卷 / 12期
关键词：
adaptive basis selection; fuzzy feature evaluation index; M-band wavelet packet frames; neural networks; remotely sensed image;
D O I：
10.1109/TGRS.2003.815398
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
The present paper describes a feature extraction method based on M-band wavelet packet frames for segmenting remotely sensed images. These wavelet features are then evaluated and selected using an efficient neurofuzzy algorithm. Both the feature extraction and neurofuzzy feature evaluation methods are unsupervised, and they, do not require the knowledge of the number and distribution of classes corresponding to various land covers in remotely sensed images. The effectiveness of the methodology is demonstrated on two four-band Indian Remote Sensing 1A satellite (IRS-1A) images containing five to six overlapping classes and a three-band SPOT image containing seven overlapping classes.
引用
收藏
页码：2900 / 2905
页数：6
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