A framework for the automatic fast target object detection of natural object like oil seeps, lost submarine and airborne using Synthetic Aperture Radar (SAR) images, implemented in MATLAB, is presented. Dark objects are detected using morphological thresholding. synthetic aperture radar (SAR) images are non-uniform and fantastically noisy image, an test or analysis is plan ahead, wherein the value of the edge of synthetic aperture radar (SAR) image in one of a kind areas like threshold area,

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... al duration with full-size one-of-a-kind uniform tiers are examined and in comparison. The results preliminarily validated that the degree the complex diploma of synthetic aperture radar (SAR) snap shots. After that, with the intention to make the automobile-segmentation efficient, the segments are also in addition processed with a sequence of strategies. On the second one degree, for each of the, a clustering based totally segmentation algorithm based totally on the Fuzzy C-means approach set of rules is followed to extract the goal. In our test, the proposed technique is examined on varieties of synthetic aperture radar SAR images, and its effectiveness is efficiently demonstrated.A dataset of different images from ALOS-PULSAR, Terra-X SAR Advanced Synthetic Aperture Radar (ASAR) were used to test the algorithm. In this paper, only preliminary results are reported.