Urban area detection based on Gabor filtering and density of local feature points

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To tackle the problem of urban area detection using high-resolution remote sensing images,this paper proposes a method based on Gabor filtering and density of local feature points by analyzing the residential area texture of high resolution image. For obtaining the amplitude information in multiple directions,the Gabor filtering was used firstly,and then the image feature points were extracted by subsequent processing of amplitude images.By computing the density of local feature points,the initial residential areas could be obtained. With further mathematical morphology transformation of the areas,the results were optimized ultimately. In the experiments,two World View2 data were used to validate the different methods. A comparative analysis with other methods shows that the method proposed in this paper has higher extraction accuracy and computational efficiency for urban area detection.