Automatic Target Recognition of Aerial Vehicles Based on Synthetic SAR Imagery Using Hybrid Stacked Denoising Auto-encoders

摘要

Deep learning classifiers, particularly, Convolutional Neural Networks (CNNs), have been demonstrated to be very effective in the area of SAR automatic target recognition (ATR). Despite of this achievement, there is still a problem with proper classification of target objects from their speckled SAR imagery. In this paper, we address this technical challenge by implementing a two-step Hybrid Stacked Denoising Auto-Encoder (HSDAE) as an effective holistic denoiser and classifier model. Since there is no publically available comprehensive real or synthetic SAR dataset of aerial vehicles, we primarily employed the IRIS Electromagnetic modeling and simulation system to generate the required synthetic noisy SAR images from an array of test physics-based CAD models placed in different operating environments. Our generated test dataset contains synthetically generated SAR images of more than 300 aerial and ground vehicles. These images are systematically scanned from various azimuth and elevation angles as well as from different ranges and in different operating environments. They are regarded as the ground truth object radiation backscattering reflectivity map of test objects. Furthermore, these images are modulated with appropriate additive multiplicative noise to form speckled SAR images. Using a partial collection of ground-truth test vehicles images along with their corresponding speckled SAR images, we train a two-step concurrent denoising auto encoder followed by a CNN model to classify vehicles. Through the initial step, a denoising operation in performed and the test objects' features like shape, size, and orientation attributes are recovered from any given input speckled SAR images. The output image from this denoising process is next passed as input to a CNN classifier for performing object recognition and classification. In this paper, we presented the architecture of HSDAE and its variants and compare their performances. Our results indicate the proposed HSDAE meets higher accuracy and repeatability for recognizing and classifying the target objects under different operating conditions.