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/

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

Recently, deep learning models, such as autoencoder, deep belief network and convolutional autoencoder (CAE), have been widely applied on polarimetric synthetic aperture radar (PolSAR) image classification task. These algorithms, however, only consider the amplitude information of the pixels in PolSAR images failing to obtain adequate discriminative features. In this work, a complex-valued convolutional autoencoder network (CV-CAE) is proposed. CV-CAE extends the encoding and decoding of CAE to complex domain so that the phase information can be adopted. Benefiting from the advantages of the CAE, CV-CAE extract features from a tiny number of training datasets. To further boost the performance, we propose a novel post processing method called spatial pixel-squares refinement (SPF) for preliminary classification map. Specifically, the majority voting and difference-value methods are utilized to determine whether the pixel-squares (PixS) needs to be refined or not. Based on the blocky structure of land cover of PolSAR images, SPF refines the PixS simultaneously. Therefore, it is more productive than current methods worked on pixel level. The proposed algorithm is measured on three typical PolSAR datasets, and better or comparable accuracy is obtained compared with other state-of-the-art methods

Air Force Institute of Technology Research Report 2018

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

SPICA:revealing the hearts of galaxies and forming planetary systems : approach and US contributions

How did the diversity of galaxies we see in the modern Universe come to be? When and where did stars within them forge the heavy elements that give rise to the complex chemistry of life? How do planetary systems, the Universe's home for life, emerge from interstellar material? Answering these questions requires techniques that penetrate dust to reveal the detailed contents and processes in obscured regions. The ESA-JAXA Space Infrared Telescope for Cosmology and Astrophysics (SPICA) mission is designed for this, with a focus on sensitive spectroscopy in the 12 to 230 micron range. SPICA offers massive sensitivity improvements with its 2.5-meter primary mirror actively cooled to below 8 K. SPICA one of 3 candidates for the ESA's Cosmic Visions M5 mission, and JAXA has is committed to their portion of the collaboration. ESA will provide the silicon-carbide telescope, science instrument assembly, satellite integration and testing, and the spacecraft bus. JAXA will provide the passive and active cooling system (supporting the