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A new Recurrent Architecture to Predict Sar Image of Target in the Absent Azimuth

The huge data collected every minute present an urgent need for image interpretation automatically. Many studies were performed previously, yet the problem was still far from being solved. For the task of detection and recognition, the preceding works usually paid more attentions to feature extraction on a single image. The azimuth information were directly ignored. This is because SAR images of target in many azimuths were absent in practical. It is therefore difficult to achieve image interpretation from the perspective of azimuth. To solve the problem, this paper develop a new recurrent architecture. We regard the images of target in the consecutive azimuths as the samples of sequence signal at the specific times. A new recurrent neural network composed of some convolutional cells is then developed. It inputs the samples prior to the current azimuth, and outputs the prediction in the next azimuth. Multiple studies are performed to demonstrate the effectiveness of proposed strategy.