Existing ground-based hyperspectral imaging (HSI) systems provide a good potential for standoff detection of explosives and their traces. The current technology, however, has still essential challenges to achieve a generic surveillance system for dynamic scenes with moving vehicles. In this regard, part I of this two-part article presents a state-of-the-art of existing HSI systems for explosive detection and discusses the future challenges for such a surveillance system. Considering the utilization of a light source and the operating spectral region, the presented overview classifies the related HSI systems as active and passive systems in the long-wave and short-wave infrared spectra, and investigates the methods in each class with respect to the targeted explosives, illumination and capturing devices, target detection algorithms, and performance evaluation methodologies. The investigation has revealed the major challenges for a generic surveillance system as 1) a thorough experimental performance validation with respect to time, date, and orientation, 2) sufficient acquisition speeds to capture moving vehicles, 3) registration and regulation of different spectral bands captured at different positions of the movement in a dynamic scene, 4) lower false positive rates required for the dynamic scenes compared to static control points, and 5) white reference compensations for the reflectance conversion in a continuous and secure surveillance system. The companion part II article then provides different solutions for reflection conversion to eliminate the dependencies to the white references for an economic and sustainable system.

The detection of gas emission levels is a crucial problem for ecology and human health. Hyperspectral image analysis offers many advantages over traditional gas detection systems with its detection capability from safe distances. Observing that the existing hyperspectral gas detection methods in the thermal range neglect the fact that the captured radiance the in longwave infrared (LWIR) spectrum is better modeled as a mixture of the radiance of background and target gases, we propose a deep learning-based hyperspectral gas detection method in this paper, which combines unmixing and classification. The proposed method first converts the radiance data to luminance-temperature data. Then, a 3D CNN and autoencoder-based network, which is specially designed for unmixing, is applied to the resulting data to acquire abundances and endmembers for each pixel. Finally, the detection is achieved by a three-layer fully connected network to detect the target gases at each pixel based on the extracted endmember spectra and abundance values. The superior performance of the proposed method with respect to the conventional hyperspectral gas detection methods using spectral angle mapper (SAM) and adaptive cosine estimator (ACE) is verified with LWIR hyperspectral images including Methane and Sulphur Dioxide gases. In addition, the ablation study with respect to different combinations of the proposed structure including direct classification and unmixing methods has revealed the contribution of the proposed system.

The recent advances in image based rendering (IBR) has pioneered a new technology, free-view television, in which TV-viewers select freely the viewing position and angle by the application of IBR on the transmitted multi-view video. Noting that the TV-viewer might also record a personal video for this arbitrarily selected view and misuse this content, it is apparent that copyright and copy protection problems also exist and should be solved for free-view TV. In this paper, we focus on this problem by proposing a watermarking method for free-view video. The watermark is embedded into every frame of multiple views by exploiting the spatial masking properties of the human visual system (HVS). Assuming that the position and rotation for the imagery view is known, the proposed method extracts the watermark successfully from an arbitrarily generated image. In order to extend the method for the case of an unknown imagery camera position and rotation, the modifications on the watermark pattern due to image based rendering operations are also analyzed. Based on this analysis, a camera position and homography estimation method is proposed considering the operations in image based rendering. The results show that the watermark detection is achieved successfully for the cases in which the imagery camera is arbitrarily located on the camera plane.

The usage of white references for reflectance conversion in a captured scene for potential ground-based hyperspectral image surveillance systems suffers from security, as it exposes the location of the system, and could be expensive for a broad application due to the high prices of standard Spectralons. In Part I of this two-part article, the state-of-the-art of ground-based hyperspectral imaging systems for explosive trace detection was discussed along with the future challenges for generic surveillance systems. The part II of the article proposes different approaches for reflection conversion to compensate the usage of white references at every capturing instant during surveillance, which is one of the main challenges of these systems. As an initial approach, recording the radiance of white reference only at the initialization of the system is tested by capturing an experimental setup in different days and times throughout a duration of two months. The correlations between the incident lights obtained from white references at different times, days, and orientations are compared. The effects of using a prerecorded white radiance for the reflectance conversion in the current scene are investigated. Second, a practical approach for reflectance calculation is tested by employing cheap-price reference objects having smooth characteristics in short-wave infrared spectrum, instead of expensive Spectralons. Finally, a novel reflectance conversion method is developed by using the reflectance of the static background materials in the scene rather than standard references by considering the stationary characteristics of surveillance systems. The proposed approaches are compared and verified on the baseline detection methods.

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 signature-based 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.