Utility of neural net classification for remote sensing data based on an improved image fusion algorithm

Bofeng Cai; Rong Yu; Zengxiang Zhang

doi:10.1117/12.712584

28 October 2006 Utility of neural net classification for remote sensing data based on an improved image fusion algorithm

Bofeng Cai, Rong Yu, Zengxiang Zhang

Author Affiliations +

Proceedings Volume 6418, Geoinformatics 2006: GNSS and Integrated Geospatial Applications; 64180C (2006) https://doi.org/10.1117/12.712584
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China

Abstract

There are many different advantages and disadvantages in traditional subpixel classification methods such as uncertain classification accuracy, etc. which bring limitations for commonly application. In recent years, many algorithms have been used to resolve these problems. In this paper, based on an optimized image fusion algorithm, a comparison experiment on traditional maximum likelihood classification and neural net classification is performed. According to the classification accuracy data, the overall accuracy of classification increased from 81.67% to 89.67%.

Citation Download Citation

Bofeng Cai, Rong Yu, and Zengxiang Zhang "Utility of neural net classification for remote sensing data based on an improved image fusion algorithm", Proc. SPIE 6418, Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 64180C (28 October 2006); https://doi.org/10.1117/12.712584

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