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检索条件"任意字段=Conference on Algorithms for Synthetic Aperture Radar Imagery IX"

共 870 条记录，以下是31-40 订阅

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Bridging the synthetic to measured SAR gap by splitting style and content 30

Bridging the synthetic to measured SAR gap by splitting styl...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Parks, Caleb Swan, Matthew Gauch, Susan Zhan, Justin Univ Arkansas Fayetteville AR 72701 USA Univ Cincinnati Cincinnati OH 45221 USA

ISBN: (纸本)9781510661547;9781510661554

Correctly classifying SAR imagery is a critical task in many applications;however, unseen samples may be difficult for machine learning models to classify. Challenges include sensitivity to slight changes in target alignment and backgrounds. To overcome this, learning algorithms require large amounts of data taken under a variety of conditions. Since collection of measured data is time-consuming, costly, and sometimes impossible, researchers utilize synthetic data to diversify existing datasets. Unfortunately, arbitrary classification algorithms do not accurately handle the domain shift from synthetic to measured images. To eliminate this shift, the community has used GAN-based image-to-image approaches to transform the synthetic images to appear measured. Our experimentation indicates some baseline approaches fail when measured data is scarce, failing to preserve labels and/or capture the full measured distribution. To alleviate this, we design a novel discriminator that separates content from style. Using this discriminator in a GAN, we force the generation procedure to preserve content while altering style. By preserving content, we avoid label-flipping and mode-seeking caused by biases in available measured data. Through extensive experiments, we show how our method is able to predict the measured distribution for out-of-sample images.

关键词： Image classification synthetic aperture radar Data modeling Machine learning Convolution Adversarial training

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3D SAR image reconstruction of ground vehicles using sparse multiple flight path data 30

3D SAR image reconstruction of ground vehicles using sparse ...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Sotirelis, Paul Gilmore, Sean Air Force Res Lab 2241 Avion Circle Wright Patterson AFB OH 45433 USA Analyt Designs Inc 245 East Gay St Columbus OH 43215 USA

ISBN: (纸本)9781510661547;9781510661554

We present an exploration of collection geometries for producing three-dimensionally (3D) focused synthetic aperture radar (SAR) derived point clouds. We consider collection geometries that can be produced by a series of continuous curves such as multiple flight paths of a fixed wing aircraft or multiple passes of a satellite orbiting the earth. As part of our analysis, we use sparse methods to reconstruct undersampled radar data. We use back-projection to focus the radar data into the spatial domain, onto a uniform volumetric grid. Additionally, we use a 3D resonance finding algorithm to extract scattering centers from volumetric radar data to produce 3D point clouds. Our analysis is based upon synthetic radar data produced using the parameters derived from our laboratory's in-door turntable inverse synthetic radar aperture (ISAR) system.(1) A key point of our analysis is to determine how many repeat passes are required to achieve a given fidelity of an object's 3D representation. Analysis will include a comparison with interferometric methods, particularly with regard to the fidelity and the point cloud density. We use a digital model of a civilian pickup truck that has been validated for use in synthetic prediction, both as a full-size model in outdoor collects as well as a reduced scale model measured indoors in our lab. Future research directions are also discussed.

关键词： synthetic aperture radar radar Interferometric synthetic aperture radar 3D modeling Point clouds

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Hybrid generative and contrastive approaches for the synthetic-measured gap 31

Hybrid generative and contrastive approaches for the synthet...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Zaunegger, Jackson S. Zelnio, Edmund Penn State Univ University Pk PA 16802 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

When performing automatic target recognition it is common to train models using synthetically generated data. This is because synthetically generated data is plentiful, and cheap to produce. Once trained on synthetic data machine learning models are often testing on measured or real-world SAR images. These models do not perform as well when analyzing measured SAR images. This problem is known as the synthetic-measured gap. In this work we explore training generative and contrastive models to close this gap. We train our models on synthetically generated data with the goal of being able to classify measured SAR images. We utilize segmentation masks as well fully-formed SAR images. In the generative approach we explore using an auto-encoder to generate segmentation masks of input SAR images. The auto-encoders architecture includes a classifier which is trained using shared features between the raw image and the segmentation mask. This model is capable of generating a segmentation mask from a SAR image. The contrastive approach uses the Sim-Siam architecture, which utilizes segmentation masks and SAR images. The contrastive model makes a classification decision, by learning features that are shared between the two input types. The goal of this work is to improve classification performance when training on synthetic data, and evaluating on measured data.

关键词： Automatic Target Recognition Auto-Encoders Contrastive Models Convolutional Networks Image Classification

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Three-dimensional spherical SAR template and feature recognition 31

Three-dimensional spherical SAR template and feature recogni...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Pepin, Matthew USAF Albuquerque NM 80840 USA

ISBN: (纸本)9781510673830;9781510673823

The high sample density and rich feature set of 3D SAR makes high performance target recognition possible even in noisy environments. The recognition performance with 3D imaging is examined for full target templates and for feature decompositions of the target. Spherical SAR with different Fourier-based focusing across different frequencies provides 3D image volumes that can be further segmented to reveal component surfaces of the target. The sparse clustering of distinct target components and the high adjacent voxel density in these cluster add to increase performance in 3D object understanding. Robust recognition depends on how well these templates and features perform across different noise levels. Target templates consist of multiple orthogonal views of the target. The components in each view can then be further segmented into points of high reflection (point scatterers), like-oriented surfaces, edge or surface clusters, or even different surface roughness components. Recognition then consists of either a whole target template or a feature aggregated target model which are both similar to the noise-free image data but different enough from the image data of dissimilar targets. This approach leads to more heuristic recognition algorithms but also leads to a more detailed understanding of the target and its most distinct features. This may lead to better target understanding, as well as, an ability to identify any feature variations from the originally imaged target model. Alternative recognition approaches or even this approach may be automated to apply to more generic 3D imaged objects, but key to a more detailed understanding is the ability to determine why or what features led to the identification.

关键词： 3D image processing synthetic aperture radar 3D acquisition Data modeling Target recognition 3D modeling Voxels Iridium Computer aided design Object recognition

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Graph Neural Network Based SAR Automatic Target Recognition with Human-in-the-loop 30

Graph Neural Network Based SAR Automatic Target Recognition ...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Zhang, Bingyi Wijeratne, Sasindu Kannan, Rajgopal Prasanna, Viktor Busart, Carl Univ Southern Calif Los Angeles CA 90007 USA DEVCOM US Army Res Lab Adelphi MD USA

ISBN: (纸本)9781510661547;9781510661554

synthetic aperture radar (SAR) automatic target recognition (ATR) is a key technique for SAR image analysis in military activities. Accurate SAR ATR can promote command and decision-making. In this work, we propose a novel SAR ATR framework with human-in-the-loop. The framework consists of a Reinforcement Learning (RL) Agent, which is followed by a GNN-based classifier. The RL A gent is capable of learning from human feedback to identify the region of target (RoT) in the SAR image. The RoT is then used to construct the input graph for the GNN classifier to perform target classification. By learning from human feedback, the RL Agent can focus on the RoT and filter out irrelevant and distracting signals in the input SAR images. We evaluate the proposed framework on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The results show that incorporating human feedback can improve classification accuracy. By visualizing the results, we observe that the RL Agent can effectively reduce irrelevant SAR signals in the input SAR images after learning from human feedback.

关键词： synthetic aperture radar automatic target recognition graph neural network human-in-the-loop

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A Sidelobe-Aware Semi-Deformable Convolutional Ship Detection Network for synthetic aperture radar imagery 7th

A Sidelobe-Aware Semi-Deformable Convolutional Ship Detectio...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Luo, Hao Lin, Xianming Xiamen Univ Minist Educ Key Lab Multimedia Trusted Percept & Efficient Co Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Nanjing Marine Radar Inst Nanjing 211153 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

This paper focus on SAR ship detection, which has been widely used in tasks such as marine traffic, fisheries management, battlefield posture assessment and military target reconnaissance. One popular solution is to utilise deep learning algorithms in conjunction with SAR image object detection. However, due to the unique imaging characteristics of SAR, existing solutions usually do not distinguish well between ships and interference targets in complex backgrounds. In this paper, we address this problem by proposing a sidelobe-aware semi-deformable convolution that takes full advantage of the combination of both standard and deformable convolution to learn high-quality ship features without significantly increasing the computational complexity. Specifically, it makes the feature extraction location more closely fit to the ship shape, strengthens the extraction capability of the region of the ship target itself and the sidelobe features, while reducing the extraction of background information. The channel attention mechanism is then proposed to enhance the ship local detail information extraction. Experiments on two widely used datasets show that the proposed method outperforms the state-of-the-art methods, which is effective and efficient to improve SAR ship detection.

关键词： Deformable Convolution synthetic aperture radar (SAR) imagery Ship Detection Remote Sensing Image Processing

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Novel Batch Active Learning Approach and Its Application to synthetic aperture radar Datasets 30

Novel Batch Active Learning Approach and Its Application to ...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Chapman, James Chen, Bohan Tan, Zheng Calder, Jeff Miller, Kevin Bertozzi, Andrea L. Univ Calif Los Angeles Dept Math 520 Portola Plaza Los Angeles CA 90095 USA Univ Minnesota Sch Math 538 Vincent Hall206 Church St SE Minneapolis MN 55455 USA

ISBN: (纸本)9781510661547;9781510661554

Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifiers performance. Recent gains have been made using sequential active learning for synthetic aperture radar (SAR) data.(1) In each iteration, sequential active learning selects a query set of size one while batch active learning selects a query set of multiple datapoints. While batch active learning methods exhibit greater efficiency, the challenge lies in maintaining model accuracy relative to sequential active learning methods. We developed a novel, two-part approach for batch active learning: Dijkstra's Annulus Core-Set (DAC) for core-set generation and LocalMax for batch sampling. The batch active learning process that combines DAC and LocalMax achieves nearly identical accuracy as sequential active learning but is more efficient, proportional to the batch size. As an application, a pipeline is built based on transfer learning feature embedding, graph learning, DAC, and LocalMax to classify the FUSAR-Ship and OpenSARShip datasets. Our pipeline outperforms the state-of-the-art CNN-based methods.*

关键词： Active learning Machine learning synthetic aperture radar Neural networks Education and training Data modeling Matrices Solid state lighting Image classification Image processing

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Detecting Ship Wake from Complex Backgrounds in synthetic aperture radar (SAR) imagery

Detecting Ship Wake from Complex Backgrounds in Synthetic Ap...

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2024 International conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Mathias, Ajisha Pradeepthi, Gubba Vivek, Ongole Kishore, Kummara R&D Institute of Science and Technology Vel Tech Rangarajan Dr.Sagunthala Department of ECE Chennai India

ISBN: (纸本)9798350369175

Ship wake identification is crucial for analyzing ocean surface synthetic aperture radar (SAR) pictures because ship wakes typically include important vessel information. A significant proportion of detection algorithms make use of the linear traits of ship wakes by converting the lines in the spatial domain into either dark or light points in a transform domain, using techniques like the Hough or Radon transforms. This study presents a novel approach for detecting ship wakes using a Gaussian Mixture Model (GMM). The method involves obtaining the Radon transform of the SAR image, which enhances the visibility of linear features. In comparison to the state of art methodologies, the findings show that the method is successful in achieving greater wake detection accuracies. © 2024 IEEE.

关键词： synthetic aperture radar

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Utilizing Contrastive Learning for Graph-Based Active Learning of SAR Data 30

Utilizing Contrastive Learning for Graph-Based Active Learni...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Brown, Jason O'Neill, Riley Calder, Jeff Bertozzi, Andrea L. Univ Calif Los Angeles Dept Math 520 Portola Plaza Los Angeles CA 90095 USA Univ Minnesota Sch Math 538 Vincent Hall206 Church St SE Minneapolis MN 55455 USA

ISBN: (纸本)9781510661547;9781510661554

Automatic target recognition with synthetic aperture radar (SAR) data is a challenging image classification problem due to the difficulty in acquiring the large labeled training sets required for conventional deep learning methods. Recent work(1) addressed this problem by utilizing powerful tools in graph-based semi-supervised learning and active learning, and achieved state of the art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset with less labeled data compared to existing techniques. A key part of the previous work was the use of unsupervised deep learning, in particular, a convolutional variational autoencoder, to embed the MSTAR images into a meaningful feature space prior to constructing the similarity graph. In this paper, we develop a contrastive SimCLR framework for feature extraction from MSTAR images by using data augmentations specific to SAR imagery. We show that our contrastive embedding results in improved performance over the variational autoencoder similarity graph method in automatic target recognition on the MSTAR dataset. We also perform a comparative study of the quality of the autoencoder and contrastive embeddings by training support vector machines (SVM) at various label rates, applying spectral clustering, and evaluating graph-cut energies, all of which show that the contrastive learning embedding is superior to the autoencoder embedding.

关键词： Contrastive Learning Active Learning synthetic aperture radar Graph-Based Learning

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Deep Semi-supervised Label Propagation for SAR Image Classification 30

Deep Semi-supervised Label Propagation for SAR Image Classif...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Enwright, Joshua Hardiman-Mostow, Harris Calder, Jeff Bertozzi, Andrea Univ Calif Los Angeles Dept Math 520 Portola Plaza Los Angeles CA 90095 USA Univ Minnesota Sch Math 538 Vincent Hall206 Church St SE Minneapolis MN 55455 USA

ISBN: (纸本)9781510661547;9781510661554

Automatic target recognition with synthetic aperture radar (SAR) data is a challenging problem due to the complexity of the images and the difficulty in acquiring labels. Recent work(1) used a convolutional variational autoencoder to extract relevant features prior to constructing a similarity graph in a graph-based active learning framework for SAR data. In this work we present two novel methods for classifying SAR data that use convolutional neural network (CNN) feature extraction together with techniques from graph-based semi-supervised learning in an end-to-end manner that can provide improved classification performance in the small labeled dataset regimes that are common in SAR ATR. First, we introduce Laplace Output Activation Neural Networks (LOAN Networks) as a way of directly optimizing feature embeddings for use with graph-based semi-supervised learning techniques. Next, we introduce Pseudo Label Propagation Neural Networks (PsLaPN Networks) as a inexpensive way to both boost the training signal as well as combat overconfidence and poor model calibration in neural networks. We present a novel derivation of simple formulas for the direct and efficient computation of derivatives of the outputs of graph-based algorithms like label propagation2 for use in the training of our networks. We test the proposed end-to-end networks for active learning on OpenSARShip, a SAR dataset, where both methods surpass the previous state-of-the-art.

关键词： Graph-based Learning Deep Learning Pseudolabeling synthetic aperture radar

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