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

Computationally efficient vessel classification using shallow neural networks on SAR data

O radar de abertura sintética (SAR) ´e um radar ativo montado em uma plataforma em movimento, que simula um comprimento de antena maior do que o comprimento real da antena física. De forma semelhante ao radar convencional, ondas eletromagnéticas são transmitidas sequencialmente e os ecos são coletados pelo radar. Com o devido processamento de sinal, este tipo de sistema ´e capaz de fornecer imagens de micro-ondas de alta resolução de uma área-alvo desejada, em praticamente todas as condições meteorológicas. Atualmente, os sistemas SAR tem sido amplamente utilizados para a deteção remota possuindo várias aplicações, como observação da superfície terrestre, cartografia e aplicações militares. Dado que ´e independente do clima e pode operar tanto de dia quanto de noite, o SAR pode ser uma fonte mais confiável quando comparado com imagens ´óticas [1]. A deteção e reconhecimento de navios em imagens SAR tornou-se um tópico importante de pesquisa nos últimos anos. Esta tese apresenta um algoritmo computacionalmente eficiente para a classificação de embarcações em imagens de SAR usando Redes Neuronais com um número reduzido de camadas, também conhecidas como shallow neural networks. A utilização de shallow networks para a classificação de embarcações será dividida em duas etapas: extração de características e classificação. A extração de características tem como objetivo reduzir a carga computacional que as deep neural networks causam nos recursos computacionais, extraindo antecipadamente características-chave da imagem SAR. Os baixos requisitos computacionais tornam esta implementação compatível com sistemas a bordo de navios e aplicações em tempo real. A classificação ´e realizada usando uma rede neural com um número reduzido de camadas, que utiliza parâmetros obtidos a partir de algoritmos de extração de características para classificar a embarcação presente na imagem de radar. O processo de extração de características processa dados do conjunto de dados Open SAR ship [2] para obter várias características da embarcação, como comprimento, largura, média, desvio padrão e o número de pontos de dispersão presentes na embarcação.Synthetic aperture radar (SAR) is an active radar that is mounted on a moving platform, simulating a longer antenna length than the physical antenna real length. Similar to a conventional radar, electromagnetic waves are sequentially transmitted and the backscattered echoes are collected by the radar. With the proper signal processing, this kind of system is able to provide high resolution microwave images of a desired target area by synthesising a larger antenna aperture, in virtually all-weather conditions. Nowadays SAR systems have been extensively used for remote sensing. It has various applications such as Earth surface monitoring, charting and militar applications. Since it is weather independent and is able to operate whether it is day or night, SAR can be a more reliable source when compared with optical imagery [1]. Ship detection and recognition in SAR images has become an importante topic in research in recent years. This thesis presents a computationally eficiente algorithm for the classification of vessels in SAR images using Neural Networks (NN) with a reduced number of hidden layers, also called Shallow Neural Networks (SNN). Herein the use of SNN for vessel classification will be divided into two main steps: feature extraction and classification. Feature extraction aims to lessen the burden deep neural networks cause on computational resources by extracting key features beforehand from the SAR image. The low computational requirements make this implementation compatible with onboard vessel systems and real time applications. The classification is implemented using a SNN that uses parameters obtained from feature extraction algorithms to classify the vessel present in the radar image. In this thesis feature extraction processes data from the Open SAR Ship dataset [2] in order to obtain the vessel’s various features, such as ship length, width, mean, standard deviation and the number of scatter points present on the vessel.N/

New Pose Estimation Methodology for Target Tracking and Identification

Ground Moving Target Indicator (GMTI) and High Resolution Radar (HRR) can track position and velocity of ground moving target. Pose, angle between position and velocity, can be derived from kinematics estimates of position and velocity and it is often used to reduce the search space of a target identification (ID) and Automatic Target Recognition (ATR)  algorithms. Due to low resolution in some radar systems, the GMTI estimated pose may exhibit large errors contributing to a faulty identification of potential targets. Our goal is to define new methodology to improve pose estimate. Besides applications in target tracking, there are numerous commercial applications in machine learning, augmented reality and body tracking

Synthetic Aperture LADAR Automatic Target Recognizer Design and Performance Prediction via Geometric Properties of Targets

Synthetic Aperture LADAR (SAL) has several phenomenology differences from Synthetic Aperture RADAR (SAR) making it a promising candidate for automatic target recognition (ATR) purposes. The diffuse nature of SAL results in more pixels on target. Optical wavelengths offers centimeter class resolution with an aperture baseline that is 10,000 times smaller than an SAR baseline. While diffuse scattering and optical wavelengths have several advantages, there are also a number of challenges. The diffuse nature of SAL leads to a more pronounced speckle effect than in the SAR case. Optical wavelengths are more susceptible to atmospheric noise, leading to distortions in formed imagery. While these advantages and disadvantages are studied and understood in theory, they have yet to be put into practice. This dissertation aims to quantify the impact switching from specular SAR to diffuse SAL has on algorithm design. In addition, a methodology for performance prediction and template generation is proposed given the geometric and physical properties of CAD models. This methodology does not rely on forming images, and alleviates the computational burden of generating multiple speckle fields and redundant ray-tracing. This dissertation intends to show that the performance of template matching ATRs on SAL imagery can be accurately and rapidly estimated by analyzing the physical and geometric properties of CAD models

Data management, chapter 5, part C

The data management for a spacecraft radar was defined in terms of an end-to-end data system, which performs the following three functions: (1) sampling and compaction of data onboard the spacecraft, (2) manipulation of radar data on the ground and (3) conversion of radar measurements to geophysical quantities by means of pattern recognition and other machine techniques. Data processing for imaging radar onboard the spacecraft was examined with the conclusion that several techniques can be used to compact the data before storage. It is recommended that compaction techniques be studied further and that existing aircraft radars be modified to provide digital data so that these compaction techniques can be tested

Sparse Signal Models for Data Augmentation in Deep Learning ATR

Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class. Recently, learning methods have shown to achieve state-of-the-art classification accuracy if abundant training data is available, sampled uniformly over the classes, and their poses. In this paper, we consider the task of ATR with a limited set of training images. We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm, such as a Convolutional neural network (CNN). The proposed data augmentation method employs a limited persistence sparse modeling approach, capitalizing on commonly observed characteristics of wide-angle synthetic aperture radar (SAR) imagery. Specifically, we exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting. Using this estimated model, we synthesize new images at poses and sub-pixel translations not available in the given data to augment CNN's training data. The experimental results show that for the training data starved region, the proposed method provides a significant gain in the resulting ATR algorithm's generalization performance.Comment: 12 pages, 5 figures, to be submitted to IEEE Transactions on Geoscience and Remote Sensin