Share Email Print
cover

Proceedings Paper

Study on classification method of TM image with artificial neural network
Author(s): Zhenhua Liu; Wen Ya; Jianbo Xu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Given the shortage of classified methods for remote sensing informations at present, the Self-organizing Artificial Neural Network is applied to classifying for TM image in order to improve classification accuracy in this paper. At the same time, as for the effecting factors of classification remote sensing image, Surface structure is considered as important parameter, which is different from other classified methods only considering spectral characters(including ENVI, Tasseled Cap, principle components, TM seven bands and etcs). Taking example for the research area of Guangzhou city, comparing with the traditional maximum likelihood classification, the result shows that the Self-organizing Artificial Neural Network is better than the supervised Maximum likelihood classification and the new method is more efficient. It is very important to provide one new mean for the classification of surface object characters in remote sensing image.

Paper Details

Date Published: 7 November 2008
PDF: 8 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 714702 (7 November 2008); doi: 10.1117/12.813202
Show Author Affiliations
Zhenhua Liu, South China Agricultural Univ. (China)
Wen Ya, South China Agricultural Univ. (China)
Jianbo Xu, South China Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top