A hybrid classification method using spectral, spatial, and textural features for remotely sensed images based on morphological filtering

Hiroshi Okumura; Makoto Yamaura; Kohei Arai

doi:10.1117/12.737635

24 October 2007 A hybrid classification method using spectral, spatial, and textural features for remotely sensed images based on morphological filtering

Hiroshi Okumura, Makoto Yamaura, Kohei Arai

Author Affiliations +

Proceedings Volume 6748, Image and Signal Processing for Remote Sensing XIII; 67481C (2007) https://doi.org/10.1117/12.737635
Event: SPIE Remote Sensing, 2007, Florence, Italy

Abstract

"HYCLASS", a new hybrid classification method for remotely sensed multi-spectral images is proposed. This method consists of two procedures, the textural edge detection and texture classification. In the textural edge detection, the maximum likelihood classification (MLH) method is employed to find "the spectral edges", and the morphological filtering is employed to process the spectral edges into "the textural edges" by sharpening the opened curve parts of the spectral edges. In the texture classification, the supervised texture classification method based on normalized Zernike moment vector that the authors have already proposed. Some experiments using a simulated texture image and an actual airborne sensor image are conducted to evaluate the classification accuracy of the HYCLASS. The experimental results show that the HYCLASS can provide reasonable classification results in comparison with those by the conventional classification method.

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Hiroshi Okumura, Makoto Yamaura, and Kohei Arai "A hybrid classification method using spectral, spatial, and textural features for remotely sensed images based on morphological filtering", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67481C (24 October 2007); https://doi.org/10.1117/12.737635

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