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Hyperspectral Image Unmixing Incorporating Adjacency Information
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Book Synopsis Hyperspectral Image Unmixing Incorporating Adjacency Information by : Bauer, Sebastian
Download or read book Hyperspectral Image Unmixing Incorporating Adjacency Information written by Bauer, Sebastian and published by KIT Scientific Publishing. This book was released on 2018-07-18 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the spectral information contained in hyperspectral images is rich, the spatial resolution of such images is in many cases very low. Many pixel spectra are mixtures of pure materials' spectra and therefore need to be decomposed into their constituents. This work investigates new decomposition methods taking into account spectral, spatial and global 3D adjacency information. This allows for faster and more accurate decomposition results.
Book Synopsis Advances in Hyperspectral Image Processing Techniques by : Chein-I Chang
Download or read book Advances in Hyperspectral Image Processing Techniques written by Chein-I Chang and published by John Wiley & Sons. This book was released on 2022-11-09 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Hyperspectral Image Processing Techniques Authoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications Advances in Hyperspectral Image Processing Techniques is derived from recent developments of hyperspectral imaging (HSI) techniques along with new applications in the field, covering many new ideas that have been explored and have led to various new directions in the past few years. The work gathers an array of disparate research into one resource and explores its numerous applications across a wide variety of disciplinary areas. In particular, it includes an introductory chapter on fundamentals of HSI and a chapter on extensive use of HSI techniques in satellite on-orbit and on-board processing to aid readers involved in these specific fields. The book’s content is based on the expertise of invited scholars and is categorized into six parts. Part I provides general theory. Part II presents various Band Selection techniques for Hyperspectral Images. Part III reviews recent developments on Compressive Sensing for Hyperspectral Imaging. Part IV includes Fusion of Hyperspectral Images. Part V covers Hyperspectral Data Unmixing. Part VI offers different views on Hyperspectral Image Classification. Specific sample topics covered in Advances in Hyperspectral Image Processing Techniques include: Two fundamental principles of hyperspectral imaging Constrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification Restricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain Hyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information Advances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis Sparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification With many applications beyond traditional remote sensing, ranging from defense and intelligence, to agriculture, to forestry, to environmental monitoring, to food safety and inspection, to medical imaging, Advances in Hyperspectral Image Processing Techniques is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field.
Book Synopsis Hyperspectral Image Processing by : Liguo Wang
Download or read book Hyperspectral Image Processing written by Liguo Wang and published by Springer. This book was released on 2015-07-15 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the authors’ research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.
Book Synopsis Hyperspectral Image Unmixing Incorporating Adjacency Information by : Sebastian Bauer
Download or read book Hyperspectral Image Unmixing Incorporating Adjacency Information written by Sebastian Bauer and published by . This book was released on 2020-10-09 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the spectral information contained in hyperspectral images is rich, the spatial resolution of such images is in many cases very low. Many pixel spectra are mixtures of pure materials' spectra and therefore need to be decomposed into their constituents. This work investigates new decomposition methods taking into account spectral, spatial and global 3D adjacency information. This allows for faster and more accurate decomposition results. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Book Synopsis Non-Linear Spectral Unmixing of Hyperspectral Data by : Somdatta Chakravortty
Download or read book Non-Linear Spectral Unmixing of Hyperspectral Data written by Somdatta Chakravortty and published by CRC Press. This book was released on 2024-08-21 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is based on satellite image processing, focusing on the potential of hyperspectral image processing (HIP) research with a case study-based approach. It covers the background, objectives, and practical issues related to HIP and substantiates the needs and potentials of said technology for discrimination of pure and mixed endmembers in pixels, including unsupervised target detection algorithms for extraction of unknown spectra of pure pixels. It includes application of machine learning and deep learning models on hyperspectral data and its role in spatial big data analytics. Features include the following: Focuses on capability of hyperspectral data in characterization of linear and non-linear interactions of a natural forest biome. Illustrates modeling the ecodynamics of mangrove habitats in the coastal ecosystem. Discusses adoption of appropriate technique for handling spatial data (with coarse resolution). Covers machine learning and deep learning models for classification. Implements non-linear spectral unmixing for identifying fractional abundance of diverse mangrove species of coastal Sundarbans. This book is aimed at researchers and graduate students in digital image processing, big data, and spatial informatics.
Book Synopsis Hyperspectral Data Processing by : Chein-I Chang
Download or read book Hyperspectral Data Processing written by Chein-I Chang and published by John Wiley & Sons. This book was released on 2013-02-01 with total page 1180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap. Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections: Part I: provides fundamentals of hyperspectral data processing Part II: offers various algorithm designs for endmember extraction Part III: derives theory for supervised linear spectral mixture analysis Part IV: designs unsupervised methods for hyperspectral image analysis Part V: explores new concepts on hyperspectral information compression Parts VI & VII: develops techniques for hyperspectral signal coding and characterization Part VIII: presents applications in multispectral imaging and magnetic resonance imaging Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages. Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject.
Book Synopsis Automatic Denoising and Unmixing in Hyperspectral Image Processing by : Honghong Peng
Download or read book Automatic Denoising and Unmixing in Hyperspectral Image Processing written by Honghong Peng and published by . This book was released on 2014 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis addresses two important aspects in hyperspectral image processing: automatic hyperspectral image denoising and unmixing. The first part of this thesis is devoted to a novel automatic optimized vector bilateral filter denoising algorithm, while the remainder concerns nonnegative matrix factorization with deterministic annealing for unsupervised unmixing in remote sensing hyperspectral images. The need for automatic hyperspectral image processing has been promoted by the development of potent hyperspectral systems, with hundreds of narrow contiguous bands, spanning the visible to the long wave infrared range of the electromagnetic spectrum. Due to the large volume of raw data generated by such sensors, automatic processing in the hyperspectral images processing chain is preferred to minimize human workload and achieve optimal result. Two of the mostly researched processing for such automatic effort are: hyperspectral image denoising, which is an important preprocessing step for almost all remote sensing tasks, and unsupervised unmixing, which decomposes the pixel spectra into a collection of endmember spectral signatures and their corresponding abundance fractions. Two new methodologies are introduced in this thesis to tackle the automatic processing problems described above. Vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios. Typical vector bilateral filtering usage does not employ parameters that have been determined to satisfy optimality criteria. This thesis also introduces an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means. The approach is based on posing the filtering problem as one of nonlinear estimation and minimizing the Stein's unbiased risk estimate (SURE) of this nonlinear estimator. Along the way, this thesis provides a plausibility argument with an analytical example as to why vector bilateral filtering outperforms band-wise 2D bilateral filtering in enhancing SNR. Experimental results show that the optimized vector bilateral filter provides improved denoising performance on multispectral images when compared to several other approaches. Non-negative matrix factorization (NMF) technique and its extensions were developed to find part based, linear representations of non-negative multivariate data. They have been shown to provide more interpretable results with realistic non-negative constrain in unsupervised learning applications such as hyperspectral imagery unmixing, image feature extraction, and data mining. This thesis extends the NMF method by incorporating deterministic annealing optimization procedure, which will help solve the non-convexity problem in NMF and provide a better choice of sparseness constrain. The approach is based on replacing the difficult non-convex optimization problem of NMF with an easier one by adding an auxiliary convex entropy constrain term and solving this first. Experiment results with hyperspectral unmixing application show that the proposed technique provides improved unmixing performance compared to other state-of-the-art methods."--Abstract.
Book Synopsis Accounting for Spectral Variability in Hyperspectral Unmixing Using Beta Endmember Distributions by : Xiaoxiao Du (Computer engineer)
Download or read book Accounting for Spectral Variability in Hyperspectral Unmixing Using Beta Endmember Distributions written by Xiaoxiao Du (Computer engineer) and published by . This book was released on 2013 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral imaging is widely used in the field of remote sensing (Goetz, et al., 1985; Green, et al., 1998). In a hyperspectral imaging system, sensors collect radiance/reflectance values over an area (or a scene) across hundreds of spectral bands (Goetz, et al., 1985). The hyperspectral image yielded by such system can be represented by a three-dimensional data cube containing those radiance/reflectance values in a range of wavelengths (Landgrebe, 2002). There are two common analysis methods for hyperspectral imagery (Hu, et al., 1999): endmember estimation and hyperspectral unmixing. Endmember estimation aims at finding pure individual spectral signatures of the materials (endmembers) in the scene (Adams, et al., 1986). Hyperspectral unmixing, on the other hand, estimates the proportions of each endmember at every pixel of the image. Each pixel in the image can then be represented by endmember spectra weighted by its corresponding proportions. In order to increase the accuracy of hyperspectral unmixing, sufficient temporal and spatial spectral variability of endmembers must be taken into consideration (Roberts, et al., 1992; Roberts, et al., 1998; Bateson, et al., 2000). The most common factors contributing to spectral variability include environmental factors, such as atmospheric effects, illumination, moisture conditions, and inherent spectral variability of the material itself, such as the variations in biophysical and biochemical composition in vegetation (Song, 2005). Under such influence, the spectral signature of endmembers may vary from time to time and from pixel to pixel in the scene. In order to account for such endmember spectral variability, endmembers are regarded as either a set, or a "bundle", of individual spectra (Roberts, et al., 1998; Bateson, et al., 2000), or as a sample from a full distribution. The application of the Normal Compositional Model with Gaussian-distributed endmembers has been discussed in the literature (Eches, et al., 2010; Zare, et al., 2012). Since the domain of Gaussian distribution is , Gaussian endmembers allow samples outside the interval of . However, in reality, the reflectance value of endmember spectra usually only vary between zero and one. Beta distributions, on the other hand, are defined only over the interval of Therefore, in this thesis, the Beta distribution is considered for endmembers in order to make it more physically realistic. The Beta Compositional Model (Zare, et al., 2013) is considered as the mixing model in this case. Two approaches based on the Normal Compositional Model (Stein, 2003; Eismann, 2006) and the Beta Compositional Model, quadratic programming (QP) approach and Metropolis-Hastings (MH) sampling approach, are presented in this thesis for hyperspectral unmixing, i.e., finding the proportions of each endmember in a hyperspectral image. QP approach determines the proportion values by minimizing the difference between the mean of Beta approximation to the convex combination of Beta endmember distributions, while MH sampling method takes both the mean and variance into consideration. Furthermore, in this thesis, unmixing algorithms that incorporate spatial information are proposed under the Beta Compositional Model (BCM-Spatial algorithms). These include algorithms based on Fuzzy Local Information C-Means Clustering Algorithm (FLICM), superpixel methods, and spatial K-means algorithm. Results indicate that unmixing algorithms based on NCM and BCM are able to successfully perform unmixing on simulated data and real hyperspectral data and can incorporate endmember spectral variability. BCM unmixing does a better job than NCM unmixing on data generated from Beta endmembers than those from Gaussian endmembers. The results from BCM-Spatial unmixing algorithms on hyperspectral image data show that the new algorithms are effective at unmixing..
Book Synopsis Processing and Analysis of Hyperspectral Data by : Jie Chen
Download or read book Processing and Analysis of Hyperspectral Data written by Jie Chen and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational, label, and data efficiency in deep learning for sparse 3D data by : Li, Lanxiao
Download or read book Computational, label, and data efficiency in deep learning for sparse 3D data written by Li, Lanxiao and published by KIT Scientific Publishing. This book was released on 2024-05-13 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.
Book Synopsis Light Field Imaging for Deflectometry by : Uhlig, David
Download or read book Light Field Imaging for Deflectometry written by Uhlig, David and published by KIT Scientific Publishing. This book was released on 2023-07-14 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optical measurement methods are becoming increasingly important for high-precision production of components and quality assurance. The increasing demand can be met by modern imaging systems with advanced optics, such as light field cameras. This work explores their use in the deflectometric measurement of specular surfaces. It presents improvements in phase unwrapping and calibration techniques, enabling high surface reconstruction accuracies using only a single monocular light field camera.
Book Synopsis Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields by : Schambach, Maximilian
Download or read book Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields written by Schambach, Maximilian and published by KIT Scientific Publishing. This book was released on 2022-10-17 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: In dieser Arbeit werden spektral kodierte multispektrale Lichtfelder untersucht, wie sie von einer Lichtfeldkamera mit einem spektral kodierten Mikrolinsenarray aufgenommen werden. Für die Rekonstruktion der kodierten Lichtfelder werden zwei Methoden entwickelt, eine basierend auf den Prinzipien des Compressed Sensing sowie eine Deep Learning Methode. Anhand neuartiger synthetischer und realer Datensätze werden die vorgeschlagenen Rekonstruktionsansätze im Detail evaluiert. -In this work, spatio-spectrally coded multispectral light fields, as taken by a light field camera with a spectrally coded microlens array, are investigated. For the reconstruction of the coded light fields, two methods, one based on the principles of compressed sensing and one deep learning approach, are developed. Using novel synthetic as well as a real-world datasets, the proposed reconstruction approaches are evaluated in detail.
Book Synopsis Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images by : Wetzel, Johannes
Download or read book Probabilistic Models and Inference for Multi-View People Detection in Overlapping Depth Images written by Wetzel, Johannes and published by KIT Scientific Publishing. This book was released on 2022-07-12 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, the task of wide-area indoor people detection in a network of depth sensors is examined. In particular, we investigate how the redundant and complementary multi-view information, including the temporal context, can be jointly leveraged to improve the detection performance. We recast the problem of multi-view people detection in overlapping depth images as an inverse problem and present a generative probabilistic framework to jointly exploit the temporal multi-view image evidence.
Book Synopsis Model-based Filtering of Interfering Signals in Ultrasonic Time Delay Estimations by : Bächle, Matthias
Download or read book Model-based Filtering of Interfering Signals in Ultrasonic Time Delay Estimations written by Bächle, Matthias and published by KIT Scientific Publishing. This book was released on 2023-01-09 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents model-based algorithmic approaches for interference-invariant time delay estimation, which are specifically suited for the estimation of small time delay differences with a necessary resolution well below the sampling time. Therefore, the methods can be applied particularly well for transit-time ultrasonic flow measurements, since the problem of interfering signals is especially prominent in this application.
Book Synopsis Machine Learning for Camera-Based Monitoring of Laser Welding Processes by : Hartung, Julia
Download or read book Machine Learning for Camera-Based Monitoring of Laser Welding Processes written by Hartung, Julia and published by KIT Scientific Publishing. This book was released on 2024-03-08 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing use of automated laser welding processes causes high demands on process monitoring. This work demonstrates methods that use a camera mounted on the focussing optics to perform pre-, in-, and post-process monitoring of welding processes. The implementation uses machine learning methods. All algorithms consider the integration into industrial processes. These challenges include a small database, limited industrial manufacturing inference hardware, and user acceptance.
Book Synopsis Hyperspectral Imaging in Agriculture, Food and Environment by : Alejandro Isabel Luna Maldonado
Download or read book Hyperspectral Imaging in Agriculture, Food and Environment written by Alejandro Isabel Luna Maldonado and published by BoD – Books on Demand. This book was released on 2018-08-01 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about the novel aspects and future trends of the hyperspectral imaging in agriculture, food, and environment. The topics covered by this book are hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables, hyperspectral imaging for assessing quality and safety of meat, multimode hyperspectral imaging for food quality and safety, models fitting to pattern recognition in hyperspectral images, sequential classification of hyperspectral images, graph construction for hyperspectral data unmixing, target visualization method to process hyperspectral image, and soil contamination mapping with hyperspectral imagery. This book is a general reference work for students, professional engineers, and readers with interest in the subject.
Book Synopsis Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation by : Prasad S. Thenkabail
Download or read book Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation written by Prasad S. Thenkabail and published by CRC Press. This book was released on 2018-12-07 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors’ perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.
