Non-linear hyperspectral unmixing with 3D convolutional encoders

Özdemir, Okan Bilge; Koz, Alper; Çetin, Yasemin Yardımcı

doi:10.1080/01431161.2022.2088258

Cited by 5 publications

(1 citation statement)

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“…The results have emphasized the importance of the unmixing part for the proposed system, which requires more sophisticated analysis in the future. In particular, the best mixing model among alternatives such as linear mixing model, augmented linear mixing model, and nonlinear mixing models [62] should be further investigated in future studies.…”

Section: Discussionmentioning

confidence: 99%

Ground-Based Hyperspectral Image Surveillance System for Explosive Detection: Methods, Experiments, and Comparisons

Kütük,

Geneci,

Bilge Özdemir

et al. 2023

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

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Explosive detection is crucial for public safety and confidence. Among various solutions for this purpose, hyperspectral imaging (HSI) differs from its alternatives with its detection capability from standoff distances. However, the state of the art for such a technology is still significantly missing a complete technical and experimental framework for surveillance applications. In this paper, an end-to-end technical framework, which involves capturing, preprocessing, reflectance conversion, target detection, and performance evaluation stages, is proposed to reveal the potential of a ground-based hyperspectral image surveillance system for the detection of explosive traces. The proposed framework utilizes a shortwave infrared region (0.9-1.7μm), which covers the distinctive absorption characteristics of different explosives. Three classes of detection methods, namely index, signature, and learning-based methods are adapted to the proposed surveillance system. Their performances are compared over various experiments, which are specifically designed for granular and sprayed residues, fingerprint residues, and explosive traces on vehicles. The experiments reveal that the best method in terms of precision and recall performances is hybrid structure detector (HSD), which effectively combines signaturebased detection with unmixing. While deep learning-based methods have also achieved satisfactory precision values, their low recall values for the moment have comparatively limited their usage for the high-risk cases. Although one of the main reasons for the current performances of deep learning methods is less data for learning, these performances for hyperspectral images can be increased with more data in the future as in other image applications.

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Section: Discussionmentioning

confidence: 99%