Segment the Roads and Residential Areas from Remote Sensing Images Using 2-D Gradients and MMAD Model

MMAD Model



Dept.of ECE


2N.Dilip Kumar

Assistant professor

Dept.of ECE



Assistant professor

Dept.of ECE



Segmentation of real-world remote sensing images is challenging because of the large size of those data, particularly for very high resolution imagery. For segmentation of remote sensing images, many algorithms have been proposed, to provide accurate results of segmentation by using this new proposed model. Here segmentation can be done by using improved 2D gradient histogram and MMAD (minimum mean absolute deviation) model. This proposed algorithm comes under ‘Thresholding’, the optimal threshold value can find by using MMAD model. Experiments on remote sensing images indicate that the new algorithm provides accurate segmentation results, particularly for images characterized by Laplace distribution histograms.

Keywords: Gradient histogram, image segmentation, minimum class mean absolute deviation, remote sensing.


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