Abstract
Automatic target recognition (ATR) can obtain important information for target surveillance from Synthetic Aperture Radar (SAR) images. Thus, a direct automatic target recognition (D-ATR) method, based on a deep neural network (DNN), is proposed in this paper. To recognize targets in large-scene SAR images, the traditional methods of SAR ATR are comprised of four major steps: detection, discrimination, feature extraction, and classification. However, the recognition performance is sensitive to each step, as the processing result from each step will affect the following step. Meanwhile, these processes are independent, which means that there is still room for processing speed improvement. The proposed D-ATR method can integrate these steps as a whole system and directly recognize targets in large-scene SAR images, by encapsulating all of the computation in a single deep convolutional neural network (DCNN). Before the DCNN, a fast sliding method is proposed to partition the large image into sub-images, to avoid information loss when resizing the input images, and to avoid the target being divided into several parts. After the DCNN, non-maximum suppression between sub-images (NMSS) is performed on the results of the sub-images, to obtain an accurate result of the large-scene SAR image. Experiments on the MSTAR dataset and large-scene SAR images (with resolution 1478 × 1784) show that the proposed method can obtain a high accuracy and fast processing speed, and out-performs other methods, such as CFAR+SVM, Region-based CNN, and YOLOv2.
Highlights
Synthetic aperture radar (SAR) is capable of working every day, in all weather conditions, and all the time, to provide high resolution images, and so it plays a significant role in surveillance and battlefield reconnaissance [1,2]
The flowchart of the proposed D-Automatic target recognition (ATR) is shown in Figure 1, which can realize the integration of target detection and recognition in large-scene SAR images
There are six parameters listed in Table 6: number of targets (No.Target) in the SAR image, number of correctly detected targets (No.Det), the proportion of targets that are correctly detected in all targets (Det Rate), number of correctly recognized targets (No Rec), the proportion of targets that are correctly recognized in all detected targets (Rec Rate), and time consumption
Summary
Synthetic aperture radar (SAR) is capable of working every day, in all weather conditions, and all the time, to provide high resolution images, and so it plays a significant role in surveillance and battlefield reconnaissance [1,2]. Many improved algorithms based on R-CNN have been proposed, such as Fast R-CNN [18] and Faster R-CNN [19], which have achieved high accuracies in recognizing targets in optical images These methods have been too computationally intensive for embedded systems and, even with high-end hardware, too slow for real-time applications. [27] proposed a region-based convolutional neural network to process the problem of SAR target recognition in large-scene images. For the sake of integrating the traditional four steps of SAR ATR as a whole system, we were encouraged by the previous works in adopting deep learning methods for target detection in optical images to the field of SAR images. The proposed D-ATR system can directly recognize targets from complex background clutter with a high accuracy and fast speed in large-scene SAR images.
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