Target Detection of Sar Image using Modified Markov Random Fields Ayed Model Segmentation Along With Google Net Classification

In the modern world the mechanism of target detection in the SAR images have huge assistance for humans to deal with complex visual signals of satellite images effectively. However, the ultimate aim of the paper was to segment the region of interest precisely from despeckling SAR images. This paper proposes a novel modified Markov random fields ayed model segmentation along with Google NET classification target detection. In the initial stage, the image gets despeckled for removing the unwanted noise. The boundaries of the images were calculated for checking the discontinuity using the canny edge detector. Then in the data reduction step by grouping the similar data items. Then the target region was segmented using the modified Markov random fields ayed model methods then the segmented output can undergo the classification process by using the Google NET CNN architecture. The proposed technique was capable of getting better results under risky conditions . Thus, the results validate the target detection of detection rate in different complexity over the existing methodology