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In this paper, a new Region-based Convolutional Neural Networks (RCNN) method is proposed for target recognition in large scene synthetic aperture radar (SAR) images. To locate and recognize the targets in SAR images, there are three steps in the traditional procedure: detection, discrimination, classification and recognition. Each step is supposed to provide optimal processing results for the next step, but this is difficult to implement in real-life applications because of speckle noise and inefficient connection among these procedures. To solve this problem, the RCNN is applied to large scene SAR target recognition, which can detect the objects while recognizing their classes based on its regression method and the sharing network structure. However, size of the input images to RCNN is limited so that the classification could be accomplished, which leads to a problem that RCNN is not able to handle the large scene SAR images directly. Thus, before the RCNN, a fast sliding method is proposed to segment the scene image into sub-images with suitable size and avoid dividing targets into different sub-images. After the RCNN, candidate regions on different slices are predicted. To locate targets on large scene SAR images from these candidate regions on small slices, the Non-maximum Suppression between Regions (NMSR) is proposed, which could find the most proper candidate region among all the overlapped regions. Experiments on 1476 × 1784 simulated MSTAR images of simple scenes and complex scenes show that the proposed method can recognize all targets with the best accuracy and fastest speed, and outperform the other methods, such as constant false alarm rate (CFAR) detector + support vector machine (SVM), Visual Attention+SVM, and Sliding-RCNN.

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The classic ship detection methods in synthetic aperture radar (SAR) images suffer from an extreme variance of ship scale. Generating a set of ship proposals before detection operation can effectively alleviate the multi-scale problem. In order to construct a scale-independent proposal generator for SAR images, we suggest four characteristics of ships in SAR images and the corresponding four procedures in this paper. Based on these characteristics and procedures, we put forward a framework to explore multi-scale ship proposals. The designed framework mainly contains two stages: hierarchical grouping and proposal scoring. Firstly, we extract edges, superpixels and strong scattering components from SAR images. The ship proposals are obtained at hierarchical grouping stage by combining the strong scattering components with superpixel grouping. Considering the difference of edge density and the completeness and tightness of contour, we obtain the scores to measure the confidence that a proposal contains a ship. Finally, the ranking proposals are obtained. Extensive experiments demonstrate the effectiveness of the four procedures. Our method achieves 0.70 the average best overlap (ABO) score, 0.59 the area under the curve (AUC) score and 0.85 best recall on a challenging dataset. In addition, the recall of our method on three scale subsets are all above 0.80. Experimental results demonstrate that our algorithm outperforms the approaches previously used for SAR images.

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In synthetic aperture radar (SAR) target recognition, the amount of target data increases continuously, and thus SAR automatic target recognition (ATR) systems are required to provide updated feature models in real time. Most recent SAR feature extraction methods have to use both existing and new samples to retrain a new model every time new data is acquired. However, this repeated calculation of existing samples leads to an increased computing cost. In this paper, a dynamic feature learning method called incremental nonnegative matrix factorization with L p sparse constraints (L p-INMF) is proposed as a solution to that problem. In contrast to conventional nonnegative matrix factorization (NMF) whereby existing and new samples are computed to retrain a new model, incremental NMF (INMF) computes only the new samples to update the trained model incrementally, which can improve the computing efficiency. Considering the sparse characteristics of scattering centers in SAR images, we set the updating process under a generic sparse constraint (L p) for matrix decomposition of INMF. Thus, L p-INMF can extract sparse characteristics in SAR images. Experimental results using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data illustrate that the proposed L p-INMF method can not only update models with new samples more efficiently than conventional NMF, but also has a higher recognition rate than NMF and INMF.