11 research outputs found
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
Convolutional neural networks (CNNs) have achieved high performance in
synthetic aperture radar (SAR) automatic target recognition (ATR). However, the
performance of CNNs depends heavily on a large amount of training data. The
insufficiency of labeled training SAR images limits the recognition performance
and even invalidates some ATR methods. Furthermore, under few labeled training
data, many existing CNNs are even ineffective. To address these challenges, we
propose a Semi-supervised SAR ATR Framework with transductive Auxiliary
Segmentation (SFAS). The proposed framework focuses on exploiting the
transductive generalization on available unlabeled samples with an auxiliary
loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR
samples and information residue loss (IRL) in training, the framework can
employ the proposed training loop process and gradually exploit the information
compilation of recognition and segmentation to construct a helpful inductive
bias and achieve high performance. Experiments conducted on the MSTAR dataset
have shown the effectiveness of our proposed SFAS for few-shot learning. The
recognition performance of 94.18\% can be achieved under 20 training samples in
each class with simultaneous accurate segmentation results. Facing variances of
EOCs, the recognition ratios are higher than 88.00\% when 10 training samples
each class
Target recognition for synthetic aperture radar imagery based on convolutional neural network feature fusion
Driven by the great success of deep convolutional neural networks (CNNs) that are currently used by quite a few computer vision applications, we extend the usability of visual-based CNNs into the synthetic aperture radar (SAR) data domain without employing transfer learning. Our SAR automatic target recognition (ATR) architecture efficiently extends the pretrained Visual Geometry Group CNN from the visual domain into the X-band SAR data domain by clustering its neuron layers, bridging the visual—SAR modality gap by fusing the features extracted from the hidden layers, and by employing a local feature matching scheme. Trials on the moving and stationary target acquisition dataset under various setups and nuisances demonstrate a highly appealing ATR performance gaining 100% and 99.79% in the 3-class and 10-class ATR problem, respectively. We also confirm the validity, robustness, and conceptual coherence of the proposed method by extending it to several state-of-the-art CNNs and commonly used local feature similarity/match metrics
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Fusing Deep Learning and Sparse Coding for SAR ATR
We propose a multimodal and multidiscipline data fusion strategy appropriate for automatic target recognition (ATR) on synthetic aperture radar imagery. Our architecture fuses a proposed clustered version of the AlexNet convolutional neural network with sparse coding theory that is extended to facilitate an adaptive elastic net optimization concept. Evaluation on the MSTAR dataset yields the highest ATR performance reported yet, which is 99.33% and 99.86% for the three- and ten-class problems, respectively
Обработка радиолокационных изображений: монография
Книга посвящена решению теоретических и практических проблем обнаружения, измерения параметров и классификации пространственно-распределённых целей (ПРЦ) по их радиолокационным изображениям (РЛИ), формируемым в многопозиционной системе наблюдения, реализованной группой космических аппаратов. В книге подробно рассмотрены методы синтеза и анализа алгоритмов классификации ПРЦ, алгоритмы оценки параметров РЛИ, алгоритмы классификации с использованием нейронных сетей, частично-когерентных РЛС, алгоритмы формирования РЛИ движущихся объектов, методы фильтрации спекл-шума, методы анализа помехоустойчивости, методы геокоррекции формируемых РЛИ.
Книга представляет интерес для специалистов, студентов и аспирантов, работающих в области разработки современных радиотехнических систем военного и гражданского назначения