An efficient electromagnetic approach to train the SVM for depth estimation of shallow buried obi ects with microwave remote sensing data

Present paper deals the fusion of image analysis with electromagnetic and support vector machine (SVM) optimization approach to estimate the depth of shallow buried metallic and dummy mine (i.e., without explosive) objects with microwave remote sensing data at X-band (i.e., 10 GHz). The objects were buried under dry and smooth sand. For this purpose, a monostatic scatterometer at X-band has been indigenously developed, which consists a transmitter and receiver mounted on the stand of the sand pit and when operated it moves over it in X- and Y- axis. An algorithm has been proposed for identification of suspected region first i.e., region of interest (ROI) that contains buried objects in the image by proposing a quantity "detection figure" (D), which further proceed for depth estimation of buried objects. Algorithm includes image processing, electromagnetic multi layer interaction and SVM approach. The convolution-using image processing techniques has been applied to avoid the overlapping of the return signal. The support vector machine (SVM) approach has been analyzed for estimation of depth and an efficient method based on electromagnetic multiplayer interaction concept has been proposed to train the SVM. The depth estimated for Al sheet gives better result than dummy landmine, but the estimated depths results for both objects are in good agreement with actual depths. The present approach may be quite helpful to develop an automatic satellite data based information systems to estimate the depth of various shallow buried objects with satellite or air-borne radar data.