Localized dictionary design for geometrically robust sonar ATR

McKay, John; Monga, Vishal; Raj, Raghu G.

doi:10.1109/igarss.2016.7729251

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…Eventually, techniques emerged which removed the need for this explicit feature engineering task. Dictionary learning methods were some of the first to forgo the explicit feature engineering path [21], [22] and automatically learn features as part of the classification process. Today, deep learning techniques are employed in the same vein [23].…”

Section: Previous Workmentioning

confidence: 99%

Structural Prior Driven Regularized Deep Learning for Sonar Image Classification

Gerg¹,

Monga²

2020

Preprint

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Deep learning has been recently shown to improve performance in the domain of synthetic aperture sonar (SAS) image classification. Given the constant resolution with range of a SAS, it is no surprise that deep learning techniques perform so well. Despite deep learning's recent success, there are still compelling open challenges in reducing the high false alarm rate and enabling success when training imagery is limited, which is a practical challenge that distinguishes the SAS classification problem from standard image classification set-ups where training imagery may be abundant. We address these challenges by exploiting prior knowledge that humans use to grasp the scene. These include unconscious elimination of the image speckle and localization of objects in the scene. We introduce a new deep learning architecture which incorporates these priors with the goal of improving automatic target recognition (ATR) from SAS imagery. Our proposal -called SPDRDL, Structural Prior Driven Regularized Deep Learning -incorporates the previously mentioned priors in a multi-task convolutional neural network (CNN) and requires no additional training data when compared to traditional SAS ATR methods. Two structural priors are enforced via regularization terms in the learning of the network:(1) structural similarity prior -enhanced imagery (often through despeckling) aids human interpretation and is semantically similar to the original imagery and (2) structural scene context priors -learned features ideally encapsulate target centering information; hence learning may be enhanced via a regularization that encourages fidelity against known ground truth target shifts (relative target position from scene center). Experiments on a challenging real-world dataset reveal that SPDRDL outperforms state-of-the-art deep learning and other competing methods for SAS image classification.

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Section: Previous Workmentioning

confidence: 99%

Structural Prior Driven Regularized Deep Learning for Sonar Image Classification

Gerg¹,

Monga²

2020

Preprint

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“…To solve (15), the most adaptable method is the iterative convex refinement (ICR) algorithm presented in [34]. Typically, we focus on choosing different α and ξ k values instead of the raw θ k , κ, and σ 2 .…”

Section: A Sparse-reconstruction Classificationmentioning

confidence: 99%

“…Contributions: The focus of this article is to effectively exploit sparsity as a prior in developing robust algorithms for classifying SONAR images. Our key contributions are: 1) Bayesian Sparsity for Target Classification: Recent work [12]- [15] has shown promise for the use of SRC for SONAR ATR. We extend these ideas by using a novel spike and slab probability distribution construction as a Bayesian prior that can provide the discriminatory nuance necessary to discern targets in Sonar images.…”

Section: Introductionmentioning

confidence: 99%

Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification

McKay

Monga

Raj

2017

IEEE Trans. Geosci. Remote Sensing

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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 arrangements, bothersome Rayleigh noise, and unavoidable background clutter. Our method, pose corrected sparsity (PCS), incorporates a novel interpretation of a spike and slab probability distribution towards use as a Bayesian prior for class-specific discrimination in combination with a dictionary learning scheme for localized patch extractions. Additionally, PCS offers the potential for anomaly detection in order to avoid false identifications of tested objects from outside the training set with no additional training required. Compelling results are shown using a database provided by the United States Naval Surface Warfare Center. Index Terms-Sonar ATR, sparse reconstruction-based classification, anomaly detection. J. McKay and V. Monga are with the

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“…McKay extracted SIFT features from sub-images of remote sensing cloud images, combining sparse reconstruction-based classification (SRC) and Localized Pose Management (LPM) algorithms for precise target recognition [19]. Yu proposed a clustering-based pattern recognition method to differentiate cloud layers and glacial snow [20].…”

Section: Introductionmentioning

confidence: 99%

Radiation Feature Fusion Dual-Attention Cloud Segmentation Network

He,

Zhang

2024

Remote Sensing

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In the field of remote sensing image analysis, the issue of cloud interference in high-resolution images has always been a challenging problem, with traditional methods often facing limitations in addressing this challenge. To this end, this study proposes an innovative solution by integrating radiative feature analysis with cutting-edge deep learning technologies, developing a refined cloud segmentation method. The core innovation lies in the development of FFASPPDANet (Feature Fusion Atrous Spatial Pyramid Pooling Dual Attention Network), a feature fusion dual attention network improved through atrous spatial convolution pooling to enhance the model’s ability to recognize cloud features. Moreover, we introduce a probabilistic thresholding method based on pixel radiation spectrum fusion, further improving the accuracy and reliability of cloud segmentation, resulting in the “FFASPPDANet+” algorithm. Experimental validation shows that FFASPPDANet+ performs exceptionally well in various complex scenarios, achieving a 99.27% accuracy rate in water bodies, a 96.79% accuracy rate in complex urban settings, and a 95.82% accuracy rate in a random test set. This research not only enhances the efficiency and accuracy of cloud segmentation in high-resolution remote sensing images but also provides a new direction and application example for the integration of deep learning with radiative algorithms.

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Localized dictionary design for geometrically robust sonar ATR

Cited by 7 publications

References 15 publications

Structural Prior Driven Regularized Deep Learning for Sonar Image Classification

Structural Prior Driven Regularized Deep Learning for Sonar Image Classification

Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification

Radiation Feature Fusion Dual-Attention Cloud Segmentation Network

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