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Hyperspectral Imaging for Landmine Detection Ihab Makki

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Hyperspectral Imaging for Landmine Detection Ihab Makki Hyperspectral Imaging for Landmine Detection Ihab Makki To cite this version: Ihab Makki. Hyperspectral Imaging for Landmine Detection. Optimization and Control [math.OC]. POLITECNICO DI TORINO, 2017. English. tel-01706356 HAL Id: tel-01706356 https://tel.archives-ouvertes.fr/tel-01706356 Submitted on 11 Feb 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. PhD Thesis Submitted in fulfillment of the requirements for the PhD degree from LEBANESE UNIVERSITY AND POLITECNICO DI TORINO Major: Electrical and Electronics Engineering Presented and defended by: Makki Ihab 12 Decembre 2017 Hyperspectral Imaging for Landmine Detection Supervised by: Prof. Rafic Younes Prof. Clovis Francis Prof. Tiziano Bianchi Prof. Massimo Zucchetti Members of the Jury: Mme Maria Sabrina Greco, Prof, University of Pisa, Reviewer M. Ali El-Zaart,Prof, Beirut Arab University Reviewer Mme Luisa Verdoliva, Dr, University of Naples Federico II Examiner M. Georges Sakr, Dr, University Saint Joseph Examiner M. Rafic Younes, Prof, Lebanese University Director M. Clovis Francis, Prof, Lebanese University Co-Director M. Massimo Zucchetti, Prof, Politecnico di Torino Director M. Tiziano Bianchi, Dr, Politecnico di Torino Co-Director M. Abdallah Fahed, Prof, Lebanese University Invited M. Bou Maroun, Pierre, Col., Lebanese army Invited Acknowledgment I would like to express my deep gratitude to my supervisors Prof. Rafic Younes, Prof. Clovis Francis, Prof Tiziano Bianchi and Prof. Massimo Zucchetti for their continuous support, kindness and availability during the three years of work on the thesis. I would like to thank the external reviewers Prof. Maria Greco and Prof. Ali El-Zaart for accepting the participation in the final dissertation and for their valuable comments that helped me in improving the quality of the report. I want to thank also the examiners Dr. Luisa Verdoliva and Dr. George Sakr for their positive contribution in the jury. A special thank goes to Prof. Fahed Abdallah and Colonnel Pierre Bou Maroun that honored me with their participation in the final defense. Also, I would like to thank the Lebanese army for providing real samples of landmines extracted from minefields in Lebanon. These samples were used in the collection of spectra of landmines in different conditions. Also I would like to thank Dr. Mohammad Awad for his help in the acquisition of the landmines’ spectra using the spectroradiometer. A special thanks goes to Lebanese University and Erasmus Committee for the financial support in Lebanon and in Italy. Finally, the constant support of my family, friends and colleagues allowed me to successfully conclude this PhD. Thank you 2 Abstract This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature 3 extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background. 4 Contents Acknowledgment ............................................................................................................... 2 Abstract .............................................................................................................................. 3 List of Figures .................................................................................................................... 9 List of Tables .................................................................................................................... 11 1. Introduction ............................................................................................................... 12 2. Problem of landmines and existing solutions ........................................................... 14 2.1. Problem of Landmines ................................................................................................. 14 Landmine contamination and impact ................................................................... 14 Types of landmines .............................................................................................. 17 2.2. Landmine detection techniques .................................................................................... 18 2.2.1. Electromagnetic Methods ..................................................................................... 19 2.2.2. Ground Penetrating Radar (GPR) ......................................................................... 19 2.2.3. Infrared/Hyperspectral Systems ........................................................................... 20 2.2.4. Acoustic/Seismic method ..................................................................................... 20 2.2.5. Nuclear Quadruple Resonance (NQR) ................................................................. 20 2.2.6. Vapor sensors ....................................................................................................... 21 2.2.7. Mechanical methods ............................................................................................. 21 3. Hyperspectral Imaging: Introduction to landmine detection and processing techniques ......................................................................................................................... 22 3.1. Introduction to Hyperspectral imaging ......................................................................... 22 Broadband, Multispectral, Hyperspectral and Ultraspectral Imaging .................. 22 Hyperspectral Image Scanning Modes ................................................................. 23 3.1.2.1. Whiskbroom or Across Track scanner ......................................................... 23 3.1.2.2. Pushbroom or Along Track scanner
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