“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Deep Belief Networks 을 이용한 주행상태 예측

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

A comprehensive and well-structured review on the application of deep learning (DL) based algorithms, such as convolutional neural networks (CNN) and long-short term memory (LSTM), in radar signal processing is given. The following DL application areas are covered: i) radar waveform and antenna array design; ii) passive or low probability of interception (LPI) radar waveform recognition; iii) automatic target recognition (ATR) based on high range resolution profiles (HRRPs), Doppler signatures, and synthetic aperture radar (SAR) images; and iv) radar jamming/clutter recognition and suppression. Although DL is unanimously praised as the ultimate solution to many bottleneck problems in most of existing works on similar topics, both the positive and the negative sides of stories about DL are checked in this work. Specifically, two limiting factors of the real-life performance of deep neural networks (DNNs), limited training samples and adversarial examples, are thoroughly examined. By investigating the relationship between the DL-based algorithms proposed in various papers and linking them together to form a full picture, this work serves as a valuable source for researchers who are seeking potential research opportunities in this promising research field.

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Deep-Learning for Radar: A Survey

Identification method of vegetable diseases based on transfer learning and attention mechanism

Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing.

In order to more efficiently mine the features of PolSAR images and build a more suitable classification model that combines the features of the polarimetric domain and the spatial domain, this article proposes a PolSAR image classification method, called low-frequency and contour subbands-driven polarimetric squeeze-and-excitation network (LC-PSENet). First, the proposed LC-PSENet introduces the nonsubsampled Laplacian pyramid to decompose polarimetric feature maps, so as to construct a multichannel PolSAR image based on the low-frequency subband and contour subband of these maps. It guides the network to perform feature mining and selection in the subbands of each polarimetric map in a supervised way, automatically balancing the contributions of polarimetric features and their subbands and the influence of interference information such as noise, making the network learning more efficient. Second, the method introduces squeeze-and-excitation operation in the convolutional neural network (CNN) to perform channel modeling on the polarimetric feature subbands. It strengthens the learning of the contributions of local maps of the polarimetric features and subbands, thereby, effectively combining the features of the polarimetric domain and the spatial domain. Experiments on the datasets of Flevoland, The Netherlands, and Oberpfaffenhofen show that the proposed LC-PSENet achieves overall accuracies of 99.66%, 99.72%, and 95.89%, which are 0.87%, 0.27%, and 1.42% higher than the baseline CNN, respectively. The isolated points in the classification results are obviously reduced, and the distinction between boundary and nonboundary is more clear and delicate. Also, the method performs better than many current state-of-the-art methods in terms of classification accuracy.

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PolSAR Image Classification Based on Low-Frequency and Contour Subbands-Driven Polarimetric SENet

Intelligent ship detection based on high-precision synthetic aperture radar (SAR) images plays a vital role in ocean monitoring and maritime management. Denoising is an effective preprocessing step for target detection. Morphological network-based denoising can effectively remove speckle noise, while the smoothing effect of which blurs the edges of the image and reduces the detection accuracy. The fusion of edge extraction and morphological network can improve detection accuracy by compensating for the lack of edge information caused by smoothing. This article proposes an end-to-end lightweight network called morphological feature-pyramid Yolo v4-tiny for SAR ship detection. First, a morphological network is introduced to preprocess the SAR images for speckle noise suppression and edge enhancement, providing spatial high-frequency information for target detection. Then, the original and preprocessed images are combined into the multichannel as an input for the convolution layer of the network. The feature pyramid fusion structure is used to extract the high-level semantic features and shallow detailed features from the image, improving the performance of multiscale target detection. Experiments on the public SAR ship detection dataset and AIR SARShip-1.0 show that the proposed method performs better than the other convolution neural network-based methods.

/pdf/sar-ship-detection-based-on-end-to-end-morphological-feature-1srkm319.pdf

SAR Ship Detection Based on End-to-End Morphological Feature Pyramid Network

Radar radio source (RRS) recognition plays an important role in the fields of military electronic support systems (ESM) and civilian autonomous driving. The rapid development of machine learning technology, especially deep learning, has effectively and efficiently improved RRS intelligent recognition performances when operating in the increasingly complex electromagnetic environment. However, the data sampling limitation and computation cost are still severe challenges in real RRS recognition scenarios. In this paper, we propose a novel network based on meta-transfer learning, called RRSARNet, to achieve effective adaptive RRS recognition in the context of low signal-to-noise ratio (SNR). First, by using the short-time Fourier transform, a six-type small samples RRS simulation dataset with different SNR levels is constructed. Then, a novel RRSARNet, based on metric learning, is proposed, which consists of a four-layer embedding module and a four-layer relational module. Finally, the RRS dataset is divided into training, supporting and testing subsets, which are used to train and test the RRSARNet in a meta-transfer learning method. Experiments on the RRS dataset show that the proposed RRSARNet can achieve an overall accuracy (OA) above 96% and 99% when the SNR is above −15 dB and −10 dB, respectively. Even when the SNR is -30 dB, OA can reach more than 70%. For 5-way 1-shot and 5-way 5-shot experiments, the inference time of an image is about 0.043 and 0.140 milliseconds, respectively. Besides, experiments on the RRS simulation dataset and the two benchmark datasets, the RRSARNet performs better or more competitive than many existing state-of-the-art technologies in terms of recognition accuracy.

RRSARNet: A Novel Network for Radar Radio Sources Adaptive Recognition

Deep-learning-based synthetic aperture radar automatic target recognition (SAR-ATR) plays a significant role in the military and civilian fields. However, data limitation and large computational cost are still severe challenges in the actual application of SAR-ATR. To improve the performance of the convolutional neural network (CNN) model with limited data samples in SAR-ATR, this article proposes a novel multidomain feature subspace fusion representation learning method, i.e., a lightweight cascaded multidomain attention network, namely, LW-CMDANet. First, we design a four-layer CNN model to perform hierarchical feature representation learning via the hinge loss function, which can efficiently alleviate the overfitting problem of the CNN model by a nongreedy training style with a small dataset. Then, a cascaded multidomain attention module, based on discrete cosine transform and discrete wavelet transform, is embedded into the previous CNN to further complete the class-specific feature extraction from both the frequency and wavelet transform domains of the input feature maps. Thus, the multidomain attention can enhance the feature extraction ability of previous nongreedy learning manner, to effectively improve the recognition accuracy of the CNN model. Experimental results on small SAR datasets show that our proposed method can achieve better or competitive performance than that of many current existing state-of-the-art methods in terms of recognition accuracy and computational cost.

https://ieeexplore.ieee.org/ielx7/4609443/4609444/09844797.pdf

Ping Lang

Papers

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing.

PolSAR Image Classification Based on Low-Frequency and Contour Subbands-Driven Polarimetric SENet

SAR Ship Detection Based on End-to-End Morphological Feature Pyramid Network

RRSARNet: A Novel Network for Radar Radio Sources Adaptive Recognition

LW-CMDANet: A Novel Attention Network for SAR Automatic Target Recognition