Remote sensing image segmentation based on an improved 2 d gradient histogram and mmad model

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REMOTE SENSING IMAGE SEGMENTATION BASED ON AN IMPROVED 2-D
GRADIENT HISTOGRAM AND MMAD MODEL
ABSTRACT
A novel remote sensing image segmentation algorithmbased on an improved 2-D
gradient histogram and minimummean absolute deviation (MMAD) model is proposed inthis
letter. We extract the global features as a 1-D histogramfrom an improved 2-D gradient
histogram by diagonal projectionand subsequently use the MMAD model on the 1-D histogram
toimplement the optimal threshold. Experiments on remote sensingimages indicate that the new
algorithm provides accurate segmentationresults, particularly for images characterized by
Laplacedistribution histograms. Furthermore, the new algorithm has lowtime consumption.

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Remote sensing image segmentation based on an improved 2 d gradient histogram and mmad model

1. REMOTE SENSING IMAGE SEGMENTATION BASED ON AN IMPROVED 2-D GRADIENT HISTOGRAM AND MMAD MODEL ABSTRACT A novel remote sensing image segmentation algorithmbased on an improved 2-D gradient histogram and minimummean absolute deviation (MMAD) model is proposed inthis letter. We extract the global features as a 1-D histogramfrom an improved 2-D gradient histogram by diagonal projectionand subsequently use the MMAD model on the 1-D histogram toimplement the optimal threshold. Experiments on remote sensingimages indicate that the new algorithm provides accurate segmentationresults, particularly for images characterized by Laplacedistribution histograms. Furthermore, the new algorithm has lowtime consumption.

Remote sensing image segmentation based on an improved 2 d gradient histogram and mmad model

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