Multi-view SAR ATR based on networks ensemble and graph search

Synthetic aperture radar automatic target recognition (SAR ATR) can be benefited from multi-view observation. It is important to consider the tradeoff between the recognition performance of the multi-view ATR system and time efficiency during reconnaissance. Here, we propose a new multi-view ATR algorithm to realize the optimal target recognition meanwhile guaranteeing the SAR platform with a short flight path. The basic scheme of the multi-view classifier based on ensemble learning is proposed, and the convolutional neural networks with two inputs are trained as the base classifiers. A directed graph forms to represent the tradeoff between the recognition performance and flight distance. Thus the optimal viewpoint selection for multi-view SAR ATR is solved by the single source shortest path search algorithm in directed graph. The multi-view SAR images are collected on the solved optimal viewpoints, and the final recognition result is obtained by the base classifiers ensemble. Some experiments based on MSTAR dataset have shown the superiority of the proposed method both on optimal viewpoint selection and recognition performance.