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Remote Sensing on 2 D Gradient Histogram and MMAD
A novel remote sensing image segmentation algorithm based on an improved 2-D gradient histogram and minimum mean absolute deviation (MMAD) model is proposed in this letter. We extract the global features as a 1-D histogram from an improved 2-D gradient histogram by diagonal projection and subsequently use the MMAD model on the 1-D histogram to implement the optimal threshold.
Experiments on remote sensingimages indicate that the new algorithm provides accurate segmentation results, particularly for images characterized by Laplace distribution histograms. Furthermore, the new algorithm has low time consumption.
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