Deep learning algorithms for detecting explosive hazards in ground penetrating radar data

Besaw, Lance E.; Stimac, Philip J.

doi:10.1117/12.2052592

Cited by 11 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3D (depth and shape) analysis [107][108][109][110][111][112][113][114][115] Advanced driver assistance systems [116][117][118][119][120] Animal detection 121 Anomaly detection 122 Automated Target Recognition [123][124][125][126][127][128][129][130][131][132][133][134] Change detection [135][136][137][138][139] Classification Data fusion 191 Dimensionality reduction 192,193 Disaster analysis/assessment 194 Environment and water analysis [195][196][197][198] Geo-information extraction 199 Human detection [200][201][202][203] Image denoising/enhancement 204,…”

Section: References Area Referencesmentioning

confidence: 99%

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Ball

Anderson

Chan

2017

J. Appl. Remote Sens

559

331

View full text Add to dashboard Cite

Abstract. In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Section: References Area Referencesmentioning

confidence: 99%

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Ball

Anderson

Chan

2017

J. Appl. Remote Sens

559

331

View full text Add to dashboard Cite

show abstract

“…At the same time, their shape can significantly change depending on the disturbance source, leading to signal profile variation effect). In fact, the problem described above is quite common when "hand-engineered" feature extraction technique is applied [5].…”

Section: The Problem Descriptionmentioning

confidence: 99%

Deep learning algorithms for signal recognition in long perimeter monitoring distributed fiber optic sensors

Макаренко¹

2016

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

View full text Add to dashboard Cite

Abstract. In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I and II) requirements and operate in difficult signal-jamming environments, with intensive signal-like jamming and a variety of changing possible signal portraits of possible recognized events. To address these issues, we have developed a two-level event detection architecture, where the primary classifier is based on an ensemble of deep convolutional networks, can recognize 7 classes of signals and receives time-space data frames as input. Using real-life data, we have shown that the applied methods result in efficient and robust multiclass detection algorithms that have a high degree of adaptability.

show abstract

“…Morton et al [7] presented an anomaly-based prescreener which automatically determined the region over which to compute the local statistics. Besaw and Stimac [8] presented a deep belief network-based method of locating buried threats. In this preliminary work, we explore the efficacy of two well-established GPR-based mine detection systems for the handheld setting after preselecting locations of interest.…”

Section: Introductionmentioning

confidence: 99%

Buried threat detection using a handheld ground penetrating radar system

et al. 2015

View full text Add to dashboard Cite

In this work, we explore the efficacy of two buried threat detectors on handheld data. The first algorithm is an energy-based algorithm, which computes how anomalous a given A-scan measurement after it is normalized according to its local statistics. It is based on a commonly used prescreener for the Husky Mounted Detection System (HMDS). In the HMDS setting measurements are sampled on a crosstrack-downtrack grid, and sequential measurements are at neighboring downtrack locations. In contrast, in the handheld setting sequential scans are often taken at neighboring crosstrack locations, and neighboring downtrack locations can be hundreds of scans away. In order to include both downtrack and crosstrack information, we compute local statistics over a much larger area than in the HMDS setting. The second algorithm is a shape-based algorithm. Shape Invariant Feature Transform (SIFT) features, which capture the gradient distributions of local patches, are extracted and used to train a non-linear Support Vector Machine (SVM). We found that in terms of AUC, the SIFT-SVM algorithm results in a 2.2% absolute improvement over the energy-based algorithm, with the greatest gains seen at lower false alarm rates.

show abstract

Deep learning algorithms for detecting explosive hazards in ground penetrating radar data

Cited by 11 publications

References 11 publications

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Deep learning algorithms for signal recognition in long perimeter monitoring distributed fiber optic sensors

Buried threat detection using a handheld ground penetrating radar system

Contact Info

Product

Resources

About