Localized Dictionary design for Geometrically Robust Sonar ATR

What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR

Finding mines in Sonar imagery is a significant problem with a great deal of relevance for seafaring military and commercial endeavors. Unfortunately, the lack of enormous Sonar image data sets has prevented automatic target recognition (ATR) algorithms from some of the same advances seen in other computer vision fields. Namely, the boom in convolutional neural nets (CNNs) which have been able to achieve incredible results - even surpassing human actors - has not been an easily feasible route for many practitioners of Sonar ATR. We demonstrate the power of one avenue to incorporating CNNs into…

Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification

Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture Sonar (SAS). Sophisticated classification techniques can now be used in Sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC) which has shown an impressive resiliency to noise, blur, and occlusion. We present a coherent strategy for expanding upon SRC for Sonar ATR that retains SRC's robustness while also being able to handle targets with diverse geometric …

Discriminative Sparsity for Sonar ATR

Advancements in Sonar image capture have enabled researchers to apply sophisticated object identification algorithms in order to locate targets of interest in images such as mines. Despite progress in this field, modern sonar automatic target recognition (ATR) approaches lack robustness to the amount of noise one would expect in real-world scenarios, the capability to handle blurring incurred from the physics of image capture, and the ability to excel with relatively few training samples. We address these challenges by adapting modern sparsity-based techniques with dictionaries comprising of t…

基于大型深度神经网络改进合成孔径声呐图像的自动目标识别

Improved Automatic Target Recognition in Synthetic Aperture Sonar Imagery Using Large Deep Neural Networks

该研究对比CNN与Transformer等DNN架构的性能，探究网络规模、预训练等因素对合成孔径声呐自动目标识别的影响，旨在生成高性能模型并提供训练路线图。

用于自动声纳处理在线适应性的地形表征：在MCM应用中对侧扫声纳应用ATR的操作经验教训

Terrain characterisation for online adaptability of automated sonar processing: Lessons learnt from operationally applying ATR to sidescan sonar in MCM applications

本文提出两种无需模型且仅需有限人工输入的在线海底表征技术，通过无监督机器学习提取地形特征，提升了AUV任务中ATR算法在复杂声纳环境下的可解释性与适应性。

Similarity-based data mining for online domain adaptation of a sonar ATR system

Due to the expensive nature of field data gathering, the lack of training data often limits the performance of Automatic Target Recognition (ATR) systems. This problem is often addressed with domain adaptation techniques, however the currently existing methods fail to satisfy the constraints of resource and time-limited underwater systems. We propose to address this issue via an online fine-tuning of the ATR algorithm using a novel data-selection method. Our proposed data-mining approach relies on visual similarity and outperforms the traditionally employed hard-mining methods. We present a co…