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    Texture Change Detection in Hyperspectral Images

    , Ph.D. Dissertation Sharif University of Technology Dianat, Rouhollah (Author) ; Kasaei, Shohreh (Supervisor)
    In this thesis, change detection in hyperspectral images is investigated. In this process, two hyperspectral images captured from the same scene but in different time instances are given and we intend to detect the occurred changes in the scene. A specific change detection algorithm contains four different steps; namely, preprocessing, selection of the criterion, postprocessing, and decision making. Dimension reduction as a critical process is also deeply investigated to be performed before classification (for decision making purposes.) Two new methods are proposed in the thesis. The proposed MCRD method is designed for dimension reduction and the MPR method is related to the change... 

    Hyperspectral Unmixing using Structured Sparse Representation

    , M.Sc. Thesis Sharif University of Technology Salehi, Fatemeh (Author) ; Rabiee, Hamid Reza (Supervisor)
    Hyperspectral imaging is one of the remote sensing methods that has been widely applied in different applications. A hyperspectral image is composed of a set of pixels showing the spectral signatures in different frequency bands recorded by sensor cells. The process that detects the proportion of pure elements in the combination of pixels is called hyperspectral unmixing. Noisy and incomplete data, high mutual coherence of spectral libraries and different sensor settings are some challenges of the unmixing problem. In this work, we focus on semi-supervised linear hyperspectral unmixing in which a spectral library is given. The resulting linear equation is an underdetermind problem with... 

    Deblurring of Hyperspectral Images via Hyperspectral Unmixing

    , M.Sc. Thesis Sharif University of Technology Mahdavi Javid, Alireza (Author) ; Amini, Arash (Supervisor)
    In this thesis, we propose a new compressed-sensing-based algorithm for unmixing of hyperspectral data, and show that the reconstruction quality could be significantly improved. In addition, we illustrate that by utilizing this approach, we can achieve an approximate estimation of the Point Spread Function (PSF) of the hyperspectral images. In this way, we first assume that the PSF belongs to a specific family of functions, such as Gaussian, then, by sweeping the parameters of the assumed PSF, we obtain the abundance coefficient matrix of the reconstructed image. Now, by choosing the sparsest coefficient matrix as the best one, we estimate the corresponding PSF. Then, we further investigate... 

    Remote Sensing of Hyperspectral Images for Detection Surface Mines

    , M.Sc. Thesis Sharif University of Technology Motahari Kelarestaghi, Alireza (Author) ; Amini, Arash (Supervisor)
    Hyperspectral unmixing (HU) is a method used to estimate the fractional abundances corresponding to endmembers in each of the mixed pixels in the hyperspectral remote sensing image. In recent times, deep learning has been recognized as an effective technique for hyperspectral image classification. In this thesis, an end-to-end HU method is proposed based on the convolutional neural network (CNN) and multi-layer perceptron (MLP). which consists of two steps: the first stage extracts features from the input data along with the inverse learning of the spectral library matrix in the hyperspectral image where columns represent the pure spectral of endmembers and The second stage is to estimate... 

    Hyperspectral Imaging Combined with Chemometric Techniques for Diagnosis of Breast Cancer

    , M.Sc. Thesis Sharif University of Technology Roshandel, Pegah (Author) ; Parastar Shahri, Hadi (Supervisor)
    Breast cancer is one of the most known types of cancer. About every eight women, one woman will suffer from one of the types of malignant tissues during her life. Diagnosing this type of cancer in the early stages is an important matter and can lead to full recovery. Therefore, one of the challenges in this field is the emergence of a fast method with high sensitivity to diagnose this disease in its early stages. Currently, biopsy is the standard method for breast cancer diagnosis. However, there are some drawbacks to this method. For instance, in order to detect the tumor margin, all breast tissue must be removed, which causes all breast tissue, including healthy tissues, to be removed.... 

    Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions

    , Article Chemometrics and Intelligent Laboratory Systems ; Volume 217 , 2021 ; 01697439 (ISSN) Sabzi, S ; Pourdarbani, R ; Rohban, M. H ; García Mateos, G ; Arribas, J. I ; Sharif University of Technology
    Elsevier B.V  2021
    In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content (mg l−1) in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and... 

    Dimension reduction of optical remote sensing images via minimum change rate deviation method

    , Article IEEE Transactions on Geoscience and Remote Sensing ; Volume 48, Issue 1 , 2010 , Pages 198-206 ; 01962892 (ISSN) Dianat, R ; Kasaei, S ; Sharif University of Technology
    This paper introduces a new dimension reduction (DR) method, called minimum change rate deviation (MCRD), which is applicable to the DR of remote sensing images. As the main shortcoming of the well-known principal component analysis (PCA) method is that it does not consider the spatial relation among image points, our proposed approach takes into account the spatial relation among neighboring image pixels while preserving all useful properties of PCA. These include uncorrelatedness property in resulted components and the decrease of error with the increasing of the number of selected components. Our proposed method can be considered as a generalization of PCA and, under certain conditions,... 

    Nondestructive nitrogen content estimation in tomato plant leaves by Vis-NIR hyperspectral imaging and regression data models

    , Article Applied Optics ; Volume 60, Issue 30 , 2021 , Pages 9560-9569 ; 1559128X (ISSN) Pourdarbani, R ; Sabzi, S ; Rohban, M. H ; García Mateos, G ; Arribas, J. I ; Sharif University of Technology
    The Optical Society  2021
    The present study aims to estimate nitrogen (N) content in tomato (Solanum lycopersicum L.) plant leaves using optimal hyperspectral imaging data by means of computational intelligence [artificial neural networks and the differential evolution algorithm (ANN-DE), partial least squares regression (PLSR), and convolutional neural network (CNN) regression] to detect potential plant stress to nutrients at early stages. First, pots containing control and treated tomato plants were prepared; three treatments (categories or classes) consisted in the application of an overdose of 30%, 60%, and 90% nitrogen fertilizer, called N-30%, N-60%, N-90%, respectively. Tomato plant leaves were then randomly... 

    Data Labelling Using Manifold-Based Semi-Supervised Learning in Multispectral Remote Sensing

    , M.Sc. Thesis Sharif University of Technology Khajenezhad, Ahmad (Author) ; Rabiee, Hamid Reza (Supervisor) ; Safari, Mohammad Ali (Co-Advisor)
    Classification of hyperspectral remote sensing images is a challenging problem, because of the small number of labeled pixels, high dimensionality of the data and large number of pixels. In this context, semisupervised learning can improve the classification accuracy by extracting information form the distribution of all the labeled and unlabeled data. Among semi-supervised methods, manifold-based algorithms have been frequently used in recent years. In most of the previous works, manifolds are constructed according to spectral representation of data, while spatial dependency of pixel labels is an important property of the images in remote sensing applications. In this thesis, after... 

    Determination of Saffron Adulteration Thorough the Package Using Hyperspectral Imaging and Chemometric Techniques

    , M.Sc. Thesis Sharif University of Technology Ostovar, Mona (Author) ; Parastar Shahri, Hadi (Supervisor)
    These days, food authenticity has become a major challenge because food health directly affects human health. The importance of authenticity is highlighted when we are faced with foods with higher nutritional and economic value. Saffron is an important example of spices because in addition to having food coloring and flavoring properties, it also has numerous health benefits, but it has limited production and high price. Therefore, with the options available and cheaper to be replaced. Thus, among the various spices, cheating in saffron has the fourth place. Hyperspectral Imaging (HSI) has been developed for food safety industrial applications. This technique combines spectroscopy and... 

    When pixels team up: Spatially weighted sparse coding for hyperspectral image classification

    , Article IEEE Geoscience and Remote Sensing Letters ; Volume 12, Issue 1 , Jan , 2015 , Pages 107-111 ; 1545598X (ISSN) Soltani Farani, A ; Rabiee, H. R ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    In this letter, a spatially weighted sparse unmixing approach is proposed as a front-end for hyperspectral image classification using a linear SVM. The idea is to partition the pixels of a hyperspectral image into a number of disjoint spatial neighborhoods. Since neighboring pixels are often composed of similar materials, their sparse codes are encouraged to have similar sparsity patterns. This is accomplished by means of a reweighted ℓ1 framework where it is assumed that fractional abundances of neighboring pixels are distributed according to a common Laplacian Scale Mixture (LSM) prior with a shared scale parameter. This shared parameter determines which endmembers contribute to the group... 

    Relationships between nonlinear and space-variant linear models in hyperspectral image unmixing

    , Article IEEE Signal Processing Letters ; Volume 24, Issue 10 , 2017 , Pages 1567-1571 ; 10709908 (ISSN) Drumetz, L ; Ehsandoust, B ; Chanussot, J ; Rivet, B ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    Hyperspectral image unmixing is a source separation problem whose goal is to identify the signatures of the materials present in the imaged scene (called endmembers), and to estimate their proportions (called abundances) in each pixel. Usually, the contributions of each material are assumed to be perfectly represented by a single spectral signature and to add up in a linear way. However, the main two limitations of this model have been identified as nonlinear mixing phenomena and spectral variability, i.e., the intraclass variability of the materials. The former limitation has been addressed by designing nonlinear mixture models, whereas the second can be dealt with by using (usually linear)... 

    Development and Application of Chemometric Methods for Hyperspectral Image Analysis for Authentication and Adulteration Detection in Food (Saffron and Turmeric)

    , Ph.D. Dissertation Sharif University of Technology Hashemi Nasab, Fatemeh Sadat (Author) ; Parastar Shahri, Hadi (Supervisor) ; Abdollahi, Hamid (Supervisor)
    The use of hyperspectral images to detect food fraud has become popular and it is necessary to develop chemometrics methods for analyzing the data from these images. Additionally, food authenticity has become a major challenge, and the focus of this thesis is on developing multivariate methods in chemometrics to extract useful information from data obtained from food authenticity verification using hyperspectral imaging (HSI). This thesis consists of six chapters. In the first chapter, a brief introduction to the fundamentals of hyperspectral imaging and food authenticity verification is presented. In the second chapter, the data structure of these images and chemometric methods including... 

    Spatial-aware dictionary learning for hyperspectral image classification

    , Article IEEE Transactions on Geoscience and Remote Sensing ; Volume 53, Issue 1 , July , 2015 , Pages 527-541 ; 01962892 (ISSN) Soltani Farani, A ; Rabiee, H. R ; Hosseini, S. A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of spectral samples. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model the pixels inside a group as members of a common subspace. That is, each pixel is represented using a linear combination of a few dictionary elements learned from the data, but since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a... 

    Visualising structural modification of patterned graphene nanoribbons using tip-enhanced Raman spectroscopy

    , Article Chemical Communications ; Volume 57, Issue 56 , 2021 , Pages 6895-6898 ; 13597345 (ISSN) Su, W ; Esfandiar, A ; Lancry, O ; Shao, J ; Kumar, N ; Chaigneau, M ; Sharif University of Technology
    Royal Society of Chemistry  2021
    Graphene nanoribbons (GNRs) fabricated using electron beam lithography are investigated using tip-enhanced Raman spectroscopy (TERS) with a spatial resolution of 5 nm under ambient conditions. High-resolution TERS imaging reveals a structurally modified 5-10 nm strip of disordered graphene at the edge of the GNRs. Furthermore, hyperspectral TERS imaging discovers the presence of nanoscale organic contaminants on the GNRs. These results pave the way for nanoscale chemical and structural characterisation of graphene-based devices using TERS. © The Royal Society of Chemistry 2021