Combined intensity and fractal information for neural classification of remote sensing imagery

Kun Shan Chen; C. F. Chen; D. W. Tsay

doi:10.1117/12.226839

17 November 1995 Combined intensity and fractal information for neural classification of remote sensing imagery

Kun Shan Chen, C. F. Chen, D. W. Tsay

Proceedings Volume 2579, Image and Signal Processing for Remote Sensing II; (1995) https://doi.org/10.1117/12.226839
Event: Satellite Remote Sensing II, 1995, Paris, France

Abstract

This paper presents the results of the terrain cover classification from satellite imagery from multispectral SPOT high resolution visible images and ERS-1 C-band SAR image. Fractal image was extracted using, from SAR, a wavelet transform as texture measure. The use of SAR fractal image to combine with SPOT data for terrain cover classification is proved to be effective and efficient, in that for SAR the despeckle process is avoided and thus naturally preserves its texture information. It was found that fractal information significantly improves the discrimination capability of the heterogeneous areas such as in urban regions, while it slightly degrades accuracy for homogeneous areas, such as open water. The overall classification performance is superior to results obtained using intensity image only.

Citation Download Citation

Kun Shan Chen, C. F. Chen, and D. W. Tsay "Combined intensity and fractal information for neural classification of remote sensing imagery", Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); https://doi.org/10.1117/12.226839

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