Classification of high spatial resolution imagery using optimal Gabor-filters-based texture features

Texture analysis has received great attention in the interpretation of high-resolution satellite images. This paper aims to find optimal filters for discriminating between residential areas and other land cover types in high spatial resolution satellite imagery. Moreover, in order to reduce the blurring border effect, inherent in texture analysis and which introduces important errors in the transition areas between different texture units, a classification procedure is designed for such high spatial resolution satellite images as follows. Firstly, residential areas are detected using Gabor texture features, and two clusters, one a residential area and the other not, are detected using the fuzzy C-Means algorithm, in the frequency space based on Gabor filters. Sequentially, a mask is generated to eliminate residential areas so that other land-cover types would be classified accurately, and not interfered with the spectrally heterogeneous residential areas. Afterwards, other objects are classified using spectral features by the MAP (maximum a posterior) - ICM (iterated conditional mode) classification algorithm designed to enforce the spatial constraints into classification. Experimental results on high spatial resolution remote sensing data confirm that the proposed algorithm provide remarkably better detection accuracy than conventional approaches in terms of both objective measurements and visual evaluation.