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Deep convolutional neural networks (DCNNs) have attracted extensive attention in synthetic aperture radar (SAR) image target detection. However, there is usually a lack of labeled data in SAR images which results in difficulties to improve the detection performance with DCNN based methods. Transfer learning borrows generic knowledge from other tasks for the target task and can be an effective solution to the small sample problem, but the features’ generality and specificity across various models are not discussed in target detection. In addition, conventional DCNN based models have too many parameters related to the prior information of the targets which increase the difficulty of transfer learning between different tasks. In this paper, we mainly conduct research in three folds to address these mentioned issues. First, the generality and specificity of features in different network models are analyzed in the field of SAR target detection. Second, a transitive transfer framework is developed for detection under small sample condition. Third, an anchor free rotatable detector with flexible structure is designed for SAR images detection to suit the transfer detection framework proposed in this paper and a reasonable explanation is given for its upper hand with small samples. Experimental results demonstrate that: 1) transitive transfer framework is valid in SAR target detection; 2) the anchor free detector in this framework is superior to the anchor based one; 3) the proposed detector achieves better performance under small sample condition by comparison with state-of-the-art methods.

Deep learning based patch-wise Synthetic Aperture Radar (SAR) image classification usually requires a large number of labeled data for training. Aiming at understanding SAR images with very limited annotation and taking full advantage of complex-valued SAR data, this paper proposes a general and practical framework for quad-, dual-, and single-polarized SAR data. In this framework, two important elements are taken into consideration: image representation and physical scattering properties. Firstly, a convolutional neural network is applied for SAR image representation. Based on time-frequency analysis and polarimetric decomposition, the scattering labels are extracted from complex SAR data with unsupervised deep learning. Then, a bag of scattering topics for a patch is obtained via topic modeling. By assuming that the generated scattering topics can be regarded as the abstract attributes of SAR images, we propose a soft constraint between scattering topics and image representations to refine the network. Finally, a classifier for land cover and land use semantic labels can be learned with only a few annotated samples. The framework is hybrid for the combination of deep neural network and explainable approaches. Experiments are conducted on Gaofen-3 complex SAR data and the results demonstrate the effectiveness of our proposed framework.

Objects in remote sensing images are difficult to detect due to arbitrarily rotated angles and the wide variance of scales. As bounding box plays an important role in object detection, we proposes a new bounding box type named rotated bounding box (rBox). With the application of rBox, we have proposed a series of detection techniques (DrBox, DrBoxLight, DrBoxSemi, DrBoxPro) to effectively handle the situation where the orientation angles of the objects are arbitrary. This article is a brief overview of these techniques. The original DrBox detector applies VGG-net as its main network framework, with image pyramid input to address multi-scale problem. DrBoxLight is a mini version of DrBox, which applies MobileNet and knowledge distillation to be deployed on embedded devices. DrBoxSemi is a semi-supervised version of DrBox, so annotation of all training samples is no longer necessary. DrBoxPro is the most important update for DrBox with professional designing of abundant prior-rBoxes on feature pyramid networks. In our experiments, we demonstrated how rBox helps to improve the performance of object detection compared with traditional bounding boxes. Besides, we evaluated the performance of our DrBox family on a series of object detection tasks.

A Siamese network based metric learning method is proposed for SAR target classification with few training samples. The network consists of two identical CNNs sharing the weights. Different from classification networks that predict the category of one sample, the Siamese network implements a metric learning to measure the similarity between two samples. Since the input is the sample pair, the amount of training data dramatically increases which contributes to training a better network. When generating the pairs, a hard negative mining scheme is proposed for improving the performance. To avoid computing the similarity between the test sample and each training sample at the test stage, which is time consuming, a two stages scheme is employed with an additional classification network taking the output of the single branch of Siamese network as the input and predicting the category. Experiments on the MSTAR dataset validate the effectiveness of the proposed method.

SAR image simulation plays an important role in the process of SAR target interpretation and recognition, especially when the number of SAR images is limited. Due to the restriction of acquisition process, the numbers of SAR target images are always insufficient. The traditional SAR image simulation, which is based on calculation of electromagnetic theory, is easily to be affected by parameter distortion due to the lack of joint optimization. Consequently, it makes a big effect on the quality of the simulated images. This paper presents a novel approach, end-to-end models, to simulate the desired images from the SAR image database. A series of generative adversarial networks include DCGAN, weight clipping WGAN and WGAN with gradient penalty are optimized and applied to generate typical SAR target images. Three kinds of network structures are used, include structure of DCGAN, newly proposed structure of four residual blocks networks and Resnet. Experimental results show that the proposed novel method is not only efficient for SAR image simulation, but also can generate excellent SAR images. Furthermore, we analysis the results and the characteristics of different networks, which pave a good way for SAR image simulation based on artificial intelligence method.

In this paper, a relaxation labelling based land masking method is proposed for separating sea and land in SAR images. Land masking, also known as sea-land segmentation, is a part of ship detection system for SAR images to avoid detecting false alarms in the land. Relaxation labeling is an iterative method, which can separate foreground pixels from background ones using the neighborhood information of pixels in the image. When relaxation labelling converges, the segmented result is often unsatisfactory, since it tends to label more foreground pixels. To overcome this issue, a loss composed of the background probability distribution diversity and the gradient magnitude of the result is introduced to indicate when to stop the iteration. Experimental results on several Gaofen-3 SAR images demonstrate the effectiveness of the proposed method.

An identity regularized sparse representation (IRSR) based SAR target recognition method is proposed in this paper. The method aims to find a transformation that can map the data to a transformed space, in which targets from the same class are close with each other, no matter the distance of them in the original space. This identity constraint can be formulated as a l 1 -norm minimization problem. By decoupling the problem into the sparse coding problem and the dictionary learning problem, the solution can be obtained iteratively. The solution is simply the weighted average of the sparse coding of all training data. Experimental results demonstrate that the proposed method is superior to several related methods.

A salient seed extraction based target detection method is proposed in this paper, aiming to distinguish target points from background points in SAR images. Different from recent superpixel based method which generates superpixels firstly, and for each superpixel decides whether it belongs to part of a target. The proposed method employs a salient point to region scheme. At first, salient seeds are extracted by mean-shift and region feature based approach. Then, pixels are assigned to the most similar seed and those assigned to the salient seeds are extracted to form the foreground region. Finally, constant false alarm rate (CFAR) operation is employed to detect the target points from the foreground region. The effectiveness of the proposed method is validated by comparing with five state-of-the-art methods on TerraSAR-X images.

Target classification is an important part in automatic target recognition (ATR) systems. Deep learning methods get state of the art performance in SAR target classification. Simulation is a useful data augmentation method when the numbers of real samples for training is not sufficient. This article discusses how to release the full potential of simulated samples which is used to improve performance of SAR target classifier. The proposed method is based on cycle adversarial network (CycleGAN), which can transfer simulated samples to be more similar with real samples in image domain. Experiments show that adding simulated samples straightforward into training dataset is not helpful to improve the performance. However, adding the transferred simulated samples for training results in about 10% increase in accuracy in the designed SAR airplane classification experiment, compared with training without data augmentation.

Inshore ship detection in SAR image faces difficulties on correctly identifying near-shore ships and onshore objects. This article proposes a multi-scale full convolutional network (MS-FCN) based sea-land segmentation method and applies a rotatable bounding box based object detection method (DR-Box) to solve the inshore ship detection problem. The sea region and land region are separated by MS-FCN then DR-Box is applied on sea region. The proposed method combines global information and local information of SAR image to achieve high accuracy. The networks are trained with Chinese Gaofen-3 satellite images. Experiments on the testing image show most inshore ships are successfully located by the proposed method.

Single image super-resolution (SR) has been widely studied in recent years as a crucial technique for remote sensing applications. This paper proposes a SR method for remote sensing images based on a transferred generative adversarial network (TGAN). Different from the previous GAN-based SR approaches, the novelty of our method mainly reflects from two aspects. First, the batch normalization layers are removed to reduce the memory consumption and the computational burden, as well as raising the accuracy. Second, our model is trained in a transfer-learning fashion to cope with the insufficiency of training data, which is the crux of applying deep learning methods to remote sensing applications. The model is firstly trained on an external dataset DIV2K and further fine-tuned with the remote sensing dataset. Our experimental results demonstrate that the proposed method is superior to SRCNN and SRGAN in terms of both the objective evaluation and the subjective perspective.

Object detection is a challenging task in computer vision. Now many detection networks can get a good detection result when applying large training dataset. However, annotating sufficient amount of data for training is often time-consuming. To address this problem, a semi-supervised learning based method is proposed in this paper. Semi-supervised learning trains detection networks with few annotated data and massive amount of unannotated data. In the proposed method, Generative Adversarial Network is applied to extract data distribution from unannotated data. The extracted information is then applied to improve the performance of detection network. Experiment shows that the method in this paper greatly improves the detection performance compared w1ith supervised learning using only few annotated data. The results prove that it is possible to achieve acceptable detection result when only few target object is annotated in the training dataset.

The edges of shadows are blurred in Synthetic Aperture Radar (SAR) images due to the moving of the radar when data are collected. This phenomenon becomes obvious in High Resolution (HR) SAR images. In this work, an adaptive approach for shadow enhancements is proposed. The performance of the shadow enhancement has some relationship with the precision of the estimation of the height and this rule is used in this work. The Height-Variant Phase Compensation (HVPC) and golden section algorithm are employed. The adaptive method is built by iterative progress of calculating the quantities of pixels corresponding to the shadow region. This method provides an automatic way for shadow enhancements and it is suitable for objects mainly composed of flat-like structures. The experiments based on the Mini-SAR of a helicopter are implemented to test the validity of the approach. The work in this paper provides a way for shadow enhancement for HR SAR images and would be useful for SAR Automatic Target Recognition.