An Adaptive Thresholding Approach Based on Improved Harris Corner Detection for Estimation of Built up Region from Remote Sensing Images
This paper proposes an approach to estimate the possible built-up areas from high-resolution remote sensing images covering different scenes for monitoring the built-up areas within limited time and minimal cost. The motivation behind this work is that the frequently recurring patterns or repeated textures corresponding to common objects of interest (e.g., built-up areas) in the input image data can help in discriminating the built-up areas from others. The proposed method consists of two steps. First step involves extracting a large set of corners from the input image by employing an improved Harris Corner detector. The improved Harris Corner selects the local maxima from the extracted corners by performing the gray scale morphological dilation operation. It then finds those points in the corner strength image that matches the dilated image and is greater than the threshold value. In the second step, an adaptive global thresholding is applied to the corner response image and binary morphological operations are performed to obtain the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in the literature in terms of detection accuracy.
KeywordsHarris corner Spectrum clustering Global thresholding
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