Enhanced Landmine Detection from Low Resolution IR Image Sequences

Wang, Tiesheng; Gu, Irene Yu‐Hua; Tjahjadi, Tardi

doi:10.1007/978-3-642-03767-2_150

Cited by 3 publications

(1 citation statement)

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“…This qualitative evaluation was confirmed by the reported results for seabed recognition. These results open the door for other applications of keypoint-based sonar imaging, including autonomous underwater vehicle navigation [52] and mine detection [53]. Future work might also pursue the development of sonar-specific local signatures.…”

Section: Discussionmentioning

confidence: 84%

Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition

Nguyen

Fablet

Ehrhold

et al. 2012

IEEE Trans. Geosci. Remote Sensing

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In this paper, we address seabed characterization and recognition in sonar images using keypoint-based approaches. Keypoint-based texture recognition has recently emerged as a powerful framework to address invariances to contrast change and geometric distortions. We investigate here to which extent keypoint-based techniques are relevant for sonar texture analysis which also involves such invariance issues. We deal with both the characterization of the visual signatures of the keypoints and the spatial patterns they form. In this respect, spatial statistics are considered. We report a quantitative evaluation for sonar seabed texture data sets comprising six texture classes such as mud, rock, and gravely sand. We clearly demonstrate the improvement brought by keypoint-based techniques compared to classical features used for sonar texture analysis such as cooccurrence and Gabor features. In this respect, we demonstrate that the joint characterization of the visual signatures of the visual keypoints and their spatial organization reaches the best recognition performances (about 97% of correct classification w.r.t. 70% and 81% using cooccurrence and Gabor features). Furthermore, the combination of difference of Gaussian keypoints and scale-invariant feature transform descriptors is recommended as the most discriminating keypoint-based framework for the analysis of sonar seabed textures.

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Section: Discussionmentioning

confidence: 84%

Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition

Nguyen

Fablet

Ehrhold

et al. 2012

IEEE Trans. Geosci. Remote Sensing

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Robust landmine detection from thermal image time series using Hough transform and rotationally invariant features

Kaya

Leloglu

Özyer

2019

International Journal of Remote Sensing

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Buried and Surface Mine Detection From Thermal Image Time Series

Kaya

Leloglu

2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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, 117 pages Since many countries suffer from existence of landmines in their territory, there is a growing demand for reliable landmine detection systems. Most of these systems require an operator to enter into the minefield. However, infrared sensor methods can be used remotely; hence, they do not put lives at risk during the search operation. In this thesis, a new approach to the infrared sensor method, which gives promising results, is presented. Buried landmines generate specific spatiotemporal thermal image patterns on the surface. Noise-reduced thermal image time series were used after preprocessing with a circularly symmetric filter different from other studies. Supervised classification methods (Support Vector Machine, Mahalanobis Discriminant Analysis, Quadratic Discriminant Analysis, and K-Nearest Neighbor) are applied on the filtered image series. Proposed method gives promising solutions that were verified with enlarged data sets. Different parameters (humidity, burial depths, training sample sizes, time intervals, seasons, spatial filter's sizes etc.) influences on the solutions were examined. To find the vi most useful time intervals, a 2-D heat transfer simulation was performed. Particularly using image series within nighttime, sunrise, and sunset times allow finding fourcentimeter deep plastic and 6.8-centimeter deep metal practice landmines with the proposed method. The best solutions were got with the 50-pixel outer diameter spatial filter and the 24-sample on this diameter. Detection rates were calculated higher in quasi-humid soil than dry soil.

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Enhanced Landmine Detection from Low Resolution IR Image Sequences

Cited by 3 publications

References 10 publications

Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition

Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition

Robust landmine detection from thermal image time series using Hough transform and rotationally invariant features

Buried and Surface Mine Detection From Thermal Image Time Series

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