Abstract—Demand towards the recognition of a target with a specific spatial signature by using remotely sensed images, the process of discovering the location, pose, and class that belongs to a particular kind of object is referred to as identification of the target. The progression of applying software and hardware to identify or recognize a goal from images of Synthetic Aperture Radar (SAR) outside the scope or within human availability is known as Automatic Target Recognition (ATR). The archaic architecture of ATR for SAR consists of three stages: recognition, distinctive, classification, and recognition. As1the time progresses many Deep1convolutional Neural Networks (DCNN) have been proposed and used for ATR-SAR and have obtained a state of the art results in many computer vision tasks, additionally shows subsequent result along the time, but most of them sort target from target chips found from SAR imagery, and used as a third stage (classification) of ATR-SAR archaic architecture. Also due to limited training images in ATR-SAR, DCNN yielded over-fitting when1directly applied to ATR-SAR. On the other hand, to make full use of SAR imagery, this paper presents Multi-View DCNN (MV-DCNN) for an end to end ATR-SAR which uses multiple views of SAR images. MV-DCNN takes1several views of a similar target. The proposed MV-DCNN proposed to instruct by the Moving1and Stationary Target Acquisition and1Recognition (MSTAR) benchmark dataset and to output scores of 10 number of classes.
 

Index Terms—automated target recognition, synthetic aperture radar, multi-view deep CNN, MSTAR
 

Cite: Sudeshna Chakraborty, Amrita, Tanupriya Choudhury, Roohi Sille, Chiranjit Dutta, and Bhupesh Kumar Dewangan, "Multi-view Deep CNN for Automated Target Recognition and Classification of Synthetic Aperture Radar Image," Journal of Advances in Information Technology, Vol. 13, No. 5, pp. 413-422, October 2022.