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

Yindi Zhao; Bo Wu

doi:10.1117/12.760812

15 August 2007 Classification of high spatial resolution imagery using optimal Gabor-filters-based texture features

Yindi Zhao, Bo Wu

Proceedings Volume 6752, Geoinformatics 2007: Remotely Sensed Data and Information; 67523F (2007) https://doi.org/10.1117/12.760812
Event: Geoinformatics 2007, 2007, Nanjing, China

Abstract

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.

Citation Download Citation

Yindi Zhao and Bo Wu "Classification of high spatial resolution imagery using optimal Gabor-filters-based texture features", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67523F (15 August 2007); https://doi.org/10.1117/12.760812

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