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.