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Classification of optical high resolution images in urban environment using spectral and textural information

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3 Author(s)
M. De Martinao ; Dept. of Biophys. & Electron. Eng., Genoa Univ., Genova, Italy ; F. Causa ; S. B. Serpico

Conventional multispectral classification methods show poor performance in the detection of urban object, due to the high within-class spectral variance of classes corresponding to complex urban areas. In this paper, to improve the classification accuracy, we propose a data fusion approach based on the joint use of spectral and spatial information provided by the texture features extracted from the Gray Level Co-occurrence Matrix (GLCM). Specifically, a three-stage process characterizes our approach. The first stage concerns texture feature extraction considering several combinations of the three GLCM parameters: window size, step and angle. In the second stage a feature selection algorithm is applied to reduce the redundancy of the feature vector composed of both spectral and texture features. The third stage is a supervised classification. Finally, we propose an adaptive approach to extract the GLCM features which exploits the spatial information provided by a conventional segmentation algorithm. The proposed approach has been tested by using IKONOS data at 4 m resolution.

Published in:

Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International  (Volume:1 )

Date of Conference:

21-25 July 2003