Study on classification method of TM image with artificial neural network

Zhenhua Liu; Wen Ya; Jianbo Xu

doi:10.1117/12.813202

7 November 2008 Study on classification method of TM image with artificial neural network

Zhenhua Liu, Wen Ya, Jianbo Xu

Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 714702 (2008) https://doi.org/10.1117/12.813202
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

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.

Citation Download Citation

Zhenhua Liu, Wen Ya, and Jianbo Xu "Study on classification method of TM image with artificial neural network", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 714702 (7 November 2008); https://doi.org/10.1117/12.813202

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