The recognition of partially occluded targets is a difficult problem in the field of synthetic aperture radar (SAR) target recognition. To eliminate the effect of occlusion, the intuitive idea is to determine the exact location and the size of the occluded area. However, this is very difficult, even impossible in practice. In order to avoid this difficulty and to improve the recognition performance for the partially occluded target, a fusion strategy of the sparse representation (SR) model based on randomly erased images is proposed to recognize the partially occluded target. The proposed method randomly erases some areas many times in both the test samples and the training samples. The erased training samples in each erasure are used to sparsely represent the corresponding erased test sample. Finally, all the SR results are fused to recognize the test sample. The proposed method utilizes random erasure to eliminate the possible occluded region. In addition, this method uses the fusion strategy to overcome under-erasing of the occluded region and erroneous erasure of the unoccluded region. The key parameter of the proposed method is the erasure ratio only. Although the erasure is random, the recognition performance of the method is relatively stable. Therefore, the method can eliminate the influence of occlusion without determining the details of occlusion. The experimental results show that the proposed method is significantly better than the state-of-the-art methods in the case of occlusion. Additionally, the recognition performance of the proposed method is similar to some comparison methods in the case of no occlusion.

Due to the ability of powerful feature representation, deep-learning-based object detection has attracted considerable research attention, and many methods have been proposed for remote sensing images. However, there are still some problems that need to be addressed. In this paper, a novel and effective detection framework based on faster region-based convolutional neural network is designed. Specifically, first, in order to locate the boundaries of large objects and find the missing small objects, DetNet is incorporated into the detection framework as the backbone network. DetNet fixes the spatial resolution in deep layers and adopts dilated bottleneck with convolution projection to increase the divergence between input and output feature maps. Then, the proposed framework uses the backbone network to extract the scene features and region features simultaneously, which are both mapped to feature vectors and then fused together. The feature fusion operation can improve the feature representation of the generated region. Last, to improve the performance of localization, the cascade structure is adopted in the framework. The cascade structure has multiple phases and every phase has independent classifier and regressor. The results obtained from the previous phase are used as the regions of interest in the next phase. Therefore, the multiphase detector can increase the detection accuracy phase by phase. Comprehensive evaluations on a public ten-class object detection dataset demonstrate the effectiveness of the proposed framework. Moreover, ablation experiments are also implemented to show the respective influence of different parts of the framework on the performance improvement.

Partially occluded target recognition is a problem that is rarely studied but must be faced in the field of synthetic aperture radar target recognition. The difficulty of this problem is finding out the location of the occluded area and eliminating the effects of occlusion. In this paper, an adaptive weighting method based on subimage sparse model is proposed to recognize the partially occluded targets. This method divides the test image (query) and the dictionary into subimages and subdictionaries by the same gridding method. Then, the sparse reconstruction error of each subimage is calculated to design weighting coefficients for the subimage. By multiplying the subimages and subdictionaries by the corresponding weighting coefficients, the weighted query and the weighted dictionary are obtained. Finally, the sparse representation recognition method is utilized to classify the weighted query. The core idea of the proposed method is that the sparse reconstruction error of the image is defined as the sum of the sparse reconstruction error of all the image pixels, and the difference of the sparse reconstruction error of the subimage between unoccluded condition and occluded condition is obvious. So the sparse reconstruction error of the subimage can be easily extracted and used as an indicator for determining the occluded area. By setting small weighting coefficients in the occluded area, the influence of occlusion is reduced and the recognition performance of partially occluded target is improved. Experimental results demonstrate that the proposed method leads to better recognition result in comparison to some state-of-the-art methods under the occlusion condition.

The local structure feature of the target in synthetic aperture radar (SAR) image and the inner correlation among multiple SAR images of the same target can effectively improve the recognition performance. In order to utilize these two kinds of information, we propose the multi-view tensor sparse representation (MTSR) model for SAR target recognition. In the proposed model, the SAR image is treated as tensor and represented by the dictionaries on each dimension of the image and corresponding sparse representation coefficient tensor (SRCT). For multi-view SAR images of one target, we design the proposed model by letting the SRCTs of those images have the same structure, which means that the non-zero elements of those SRCTs have the same coordinates. In order to solve the proposed model, we propose the joint tensor orthogonal matching pursuit (JT-OMP) algorithm to calculate the SRCTs of multiple views. JT-OMP ensures that all the SRCTs have the same structure by looking for the atoms of dictionaries that can contribute the total maximum energy for all multi-view tensors in every iteration. To achieve recognition, we construct two dictionaries for each class and compare the total sparse representation error of multi-view SAR images in each class. The experiments conducted on the moving and stationary target acquisition recognition database (MSTAR) verify the performance of the proposed algorithm.

This paper introduces a novel classification strategy based on multi-dimensional sparse model for target recognition in Synthetic Aperture Radar (SAR) image. This method constructs dictionaries for each dimensions of SAR image instead of a single dictionary in traditional sparse model. In dictionary learning stage, this method adopts a dictionary-by-dictionary updating strategy. Under the premise of keeping the other dictionaries unchanged, each dictionary is updated using a traditional dictionary learning method. The sparsity coefficients of test samples in various categories of dictionaries are first calculated separately, and the test samples are classified using the sparse representation minimum error criterion. The experimental results on the MSTAR dataset show that compared with the traditional sparse models, this method can improve the target recognition rate while reducing the volume of dictionary. Therefore, in the field of SAR target identification, multidimensional sparse models have better recognition capabilities than traditional sparse models.