Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

Chaudhary, Siddharth; Ninsawat, Sarawut; Nakamura, Tai

doi:10.3390/s19010097

Cited by 16 publications

(9 citation statements)

References 55 publications

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“…The approach used to achieve this objective is shown in Figures 3 and 4. The experiments were conducted in outdoor conditions and suitable altitude and minimum mapping size of the chemicals were identified using the SVM model and spectral library developed in [19]. The principal component analysis and first derivative were used to select 22 optimal wavelengths out of 144 wavelengths in the range of 400-1000 nm.…”

Section: Methodsmentioning

confidence: 99%

Influence of Altitude and Image Overlap on Minimum Mapping Size of Chemical in Non-Destructive Trace Detection Using Hyperspectral Remote Sensing

Chaudhary¹,

Ninsawat²,

Nakamura³

2021

Applied Sciences

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The increasing threat of explosives is a serious issue affecting socio-economy of many countries at multiple levels, such as public security, unused arable land, closing of trade routes, isolation of villages, and these act as a hindrance in the development of the country. Activities using explosives have increased in the last two decades making it a global threat that is challenging humanity. In this study, different chemicals such as Ammonium Nitrate (AN), Trinitrotoluene (TNT) and C4 along with soil as the background material were used for trace detection. The aim of this study was to determine an altitude for the sensor and to identify the minimum mapping size of the chemical at which the model can achieve 70% accuracy. To determine the altitude and minimum size of the chemical that can be detected in the acceptable range of accuracy, several experiments were performed in real ground conditions. This study focuses on the applicability of the proposed method in the real world. In the first set of experiments, the altitude of the sensor was varied from 40 cm to 150 cm and the accuracy of the model was determined. From the analysis, it was found that the model achieved more than 75% accuracy at an altitude of 90 cm with an image overlap of 70%. In the second set of experiments, the minimum size of chemical sample was varied from 0.25 cm to 1 cm. The accuracy of the model was more than 70% when the minimum sample size was 0.5 cm or greater. For various altitude determined, the speed of the vehicle was calculated. Therefore, to implement hyperspectral imaging system on the unarmed vehicle for real application, the suggested altitude and speed of the sensor should be around 90 cm and 10.5 cm/s at which detection limit would be equal or more than 0.5 cm with accuracy greater than 70%.

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Section: Methodsmentioning

confidence: 99%

Influence of Altitude and Image Overlap on Minimum Mapping Size of Chemical in Non-Destructive Trace Detection Using Hyperspectral Remote Sensing

Chaudhary¹,

Ninsawat²,

Nakamura³

2021

Applied Sciences

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“…In 2022, Andrea Genangeli of the University of Florence et al used the ANN-AP model to classify apples of different qualities with an accuracy of up to 97% [8]. In addition, thanks to the ability of hyperspectral detection technology to distinguish material characteristics, hyperspectral imaging has also been used to identify impurities in cotton [9], industrial assembly lines [10,11], water [12,13] and soil [14]. It can be said that the hyperspectral target detection system has shown great application potential in industrial production applications.…”

Section: Introductionmentioning

confidence: 99%

High-speed hyperspectral target detection system based on high efficiency spectroscopy and illumination

Cui,

Xin,

Zhicheng

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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In this paper, a high-speed hyperspectral target detection system based on high-efficiency spectrographs and illumination devices is proposed. The system includes a hyperspectral imager, an illumination system, and a data processing system with spectral target recognition. The system can be used for fast impurity rejection on industrial lines. By adopting a high diffraction efficiency grating and a low distortion spectral spectroscopic system, the system realizes spectral imaging with high throughput and low distortion. Compact linear light source is used to achieve high irradiance full-spectrum illumination. The edge computing system adopts a spectral target recognition method combining CTBS with RTCEM and RTRAD. The spectral range of the system is 400 nm to 1000nm, the spectral resolution is 5 nm. The system can be used for the assembly line with a transportation speed of 1m/s, and the unknown debris detection with an accuracy of more than 87% and the known debris detection with an accuracy of 96% can be carried out on debris with a size of less than 2 mm. The method proposed in this paper increases the detection speed of existing hyperspectral detection systems by more than three times, which is expected to improve the practicability of hyperspectral detection technology in the field of industrial production.

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“…Hyperspectral image (HSI) processing is one of the core research areas in the field of HSI remote sensing [ 1 , 2 , 3 , 4 , 5 ], which is an important means of earth observation. HSIs have many spectral bands and large amounts of data, many of which are redundant.…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees

Liu

Wang³

et al. 2020

Entropy

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At present, many Deep Neural Network (DNN) methods have been widely used for hyperspectral image classification. Promising classification results have been obtained by utilizing such models. However, due to the complexity and depth of the model, increasing the number of model parameters may lead to an overfitting of the model, especially when training data are insufficient. As the performance of the model mainly depends on sufficient data and a large network with reasonably optimized hyperparameters, using DNNs for classification requires better hardware conditions and sufficient training time. This paper proposes a feature fusion and multi-layered gradient boosting decision tree model (FF-DT) for hyperspectral image classification. First, we fuse extended morphology profiles (EMPs), linear multi-scale spatial characteristics, and nonlinear multi-scale spatial characteristics as final features to extract both special and spectral features. Furthermore, a multi-layered gradient boosting decision tree model is constructed for classification. We conduct experiments based on three datasets, which in this paper are referred to as the Pavia University, Indiana Pines, and Salinas datasets. It is shown that the proposed FF-DT achieves better performance in classification accuracy, training conditions, and time consumption than other current classical hyperspectral image classification methods.

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Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

Cited by 16 publications

References 55 publications

Influence of Altitude and Image Overlap on Minimum Mapping Size of Chemical in Non-Destructive Trace Detection Using Hyperspectral Remote Sensing

Influence of Altitude and Image Overlap on Minimum Mapping Size of Chemical in Non-Destructive Trace Detection Using Hyperspectral Remote Sensing

High-speed hyperspectral target detection system based on high efficiency spectroscopy and illumination

A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees

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