Download or read book Multi Geometry Atmospheric Correction and Target Spectra Retrieval from Hyperspectral Images Via Deep Learning written by Fangcao Xu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Atmospheric correction is a fundamental task in remote sensing because observations are taken either of the atmosphere or looking through the atmosphere. Atmospheric correction errors can significantly alter the spectral signature of the observations, and lead to invalid classifications or target detection. This is even more crucial when working with hyperspectral data, where a precise measurement of spectral properties is required. State-of-the-art physical approaches for atmospheric correction require extensive prior knowledge about sensor characteristics, collection geometry, and environmental characteristics of the scene being collected. These approaches are computationally expensive, prone to inaccuracy due to lack of sufficient environmental and collection information, and often impossible for real-time applications. Recently, artificial intelligence (AI) and advanced deep learning (DL) techniques have obtained great achievements in many research areas, such as target detection, image classification and segmentation, and spatiotemporal analysis. To take full advantage of remote sensing observation in quickly and reliably acquiring data for a large area, integrating AI with remote sensing and GIScience could provide an automatic and efficient processing tool and discover knowledge that has never been revealed from massive datasets. In this dissertation, I propose three major research topics to expand the solution of current remote sensing image analysis for full geometric diversity to exploit multi-scans hyperspectral images simultaneously and incorporate deep neural networks. Three studies are conducted with simulated and real-world collected hyperspectral images for a full spectrum analysis, ranging from (0.4 - 13.5 um). The first study investigates the longwave infrared spectrum on the simulated data to understand the impact of different solar and atmospheric radiative components on the at-sensor signature under various geometries. The goal is to develop and test a general deep learning solution for atmospheric correction and target detection using multiple hyperspectral scenes. The second study proposes a geometry-dependent hybrid neural network that implements the causality of different geometric factors into the network structure. This network is trained on two different longwave hyperspectral dataset, one simulated using MODTRAN, and the second observed using the Blue Heron instrument in a dedicated field study. The third study focuses on the visible, near infrared and shortwave infrared spectrum, to improve the time-dependency of the network and represent the seasonal and diurnal characteristics of atmosphere and solar radiance. The main contributions of this dissertation are: 1) it makes use of the computer ability with new innovative AI methods and multi-scan hyperspectral data, which can better learn the non-linear relationship and complex interactions between atmosphere and different radiative components passing through it, and 2) it enhances the current state-of-the-science in hyperspectral remote sensing research and drives future hyperspectral sensor performance requirements and concepts of atmospheric characterization and target detection operations.

Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.

Download or read book Deep Learning for Hyperspectral Image Analysis and Classification written by Linmi Tao and published by Springer Nature. This book was released on 2021-02-20 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

Download or read book A New Method for Atmospheric Correction and Aerosol Optical Property Retrieval for VIS SWIR Multi and Hyperspectral Imaging Sensors QUAC QUick Atmospheric Correction written by and published by . This book was released on 2005 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: We describe a new VNIR-SWIR atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for realtime applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. In this paper, QUAC is applied to atmospherically correction several AVIRIS data sets. Comparisons to the physics-based FLAASH code are also presented.

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.

Download or read book Processing and Analysis of Hyperspectral Data written by Jie Chen and published by BoD – Books on Demand. This book was released on 2020-01-22 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods.

Download or read book Atmospheric Correction Algorithm for Hyperspectral Imagery written by and published by . This book was released on 1999 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: In December 1997, the US Department of Energy (DOE) established a Center of Excellence (Hyperspectral-Multispectral Algorithm Research Center, HyMARC) for promoting the research and development of algorithms to exploit spectral imagery. This center is located at the DOE Remote Sensing Laboratory in Las Vegas, Nevada, and is operated for the DOE by Bechtel Nevada. This paper presents the results to date of a research project begun at the center during 1998 to investigate the correction of hyperspectral data for atmospheric aerosols. Results of a project conducted by the Rochester Institute of Technology to define, implement, and test procedures for absolute calibration and correction of hyperspectral data to absolute units of high spectral resolution imagery will be presented. Hybrid techniques for atmospheric correction using image or spectral scene data coupled through radiative propagation models will be specifically addressed. Results of this effort to analyze HYDICE sensor data will be included. Preliminary results based on studying the performance of standard routines, such as Atmospheric Pre-corrected Differential Absorption and Nonlinear Least Squares Spectral Fit, in retrieving reflectance spectra show overall reflectance retrieval errors of approximately one to two reflectance units in the 0.4- to 2.5-micron-wavelength region (outside of the absorption features). These results are based on HYDICE sensor data collected from the Southern Great Plains Atmospheric Radiation Measurement site during overflights conducted in July of 1997. Results of an upgrade made in the model-based atmospheric correction techniques, which take advantage of updates made to the moderate resolution atmospheric transmittance model (MODTRAN 4.0) software, will also be presented. Data will be shown to demonstrate how the reflectance retrieval in the shorter wavelengths of the blue-green region will be improved because of enhanced modeling of multiple scattering effects.

Download or read book Handbook of Blind Source Separation written by Pierre Comon and published by Academic Press. This book was released on 2010-02-17 with total page 856 pages. Available in PDF, EPUB and Kindle. Book excerpt: Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. - Covers the principles and major techniques and methods in one book - Edited by the pioneers in the field with contributions from 34 of the world's experts - Describes the main existing numerical algorithms and gives practical advice on their design - Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications - Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications

Download or read book Spectral and Polarimetric Analysis of Hyperspectral Data Collected by an Acousto optic Tunable Filter System written by Melissa A. Sturgeon and published by . This book was released on 1993 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of data collected during a ground-based experiment of an acousto-optic tunable filter (AOTF) hyperspectral imaging system illustrates the utility of this technology for military applications. The unique ability of an AOTF system to simultaneously acquire two orthogonally polarized images allows both spectral and polarimetric characterization of targets. The data images selected for analysis contain camouflaged military equipment deployed in a desert background. After geometric and atmospheric correction, spectral analysis is accomplished using two different methods. Comparing images created by the difference between polarizations for each band provides the basis for polarimetric analysis of the data. An algorithm developed to combine the information provided by spectral and polarimetric analysis shows how features within a scene can be distinguished from the background. Results show that AOTF hyperspectral technology has potential to enhance current military intelligence collection capabilities.

Download or read book Hyperspectral Remote Sensing written by Prem Chandra Pandey and published by Elsevier. This book was released on 2020-08-05 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology. - Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines - Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection - Provides an overview of the state-of-the-art, including algorithms, techniques and case studies

Download or read book Machine Learning Approaches for Urban Computing written by Mainak Bandyopadhyay and published by Springer Nature. This book was released on 2021-04-28 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses various machine learning applications and models, developed using heterogeneous data, which helps in a comprehensive prediction, optimization, association analysis, cluster analysis and classification-related applications for various activities in urban area. It details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data. The book is helpful for researchers, academicians, faculties, scientists and geospatial industry professionals for their research work and sets new ideas in the field of urban computing.

Download or read book Hyperspectral Remote Sensing written by Marcus Borengasser and published by CRC Press. This book was released on 2007-12-13 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Land management issues, such as mapping tree species, recognizing invasive plants, and identifying key geologic features, require an understanding of complex technical issues before the best decisions can be made. Hyperspectral remote sensing is one the technologies that can help with reliable detection and identification. Presenting the fundamenta

Download or read book Airborne Measurements for Environmental Research written by Manfred Wendisch and published by John Wiley & Sons. This book was released on 2013-03-22 with total page 659 pages. Available in PDF, EPUB and Kindle. Book excerpt: This first comprehensive review of airborne measurement principles covers all atmospheric components and surface parameters. It describes the common techniques to characterize aerosol particles and cloud/precipitation elements, while also explaining radiation quantities and pertinent hyperspectral and active remote sensing measurement techniques along the way. As a result, the major principles of operation are introduced and exemplified using specific instruments, treating both classic and emerging measurement techniques. The two editors head an international community of eminent scientists, all of them accepted and experienced specialists in their field, who help readers to understand specific problems related to airborne research, such as immanent uncertainties and limitations. They also provide guidance on the suitability of instruments to measure certain parameters and to select the correct type of device. While primarily intended for climate, geophysical and atmospheric researchers, its relevance to solar system objects makes this work equally appealing to astronomers studying atmospheres of solar system bodies with telescopes and space probes.

Download or read book Thriving on Our Changing Planet written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-01-20 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet presents prioritized science, applications, and observations, along with related strategic and programmatic guidance, to support the U.S. civil space Earth observation program over the coming decade.

Download or read book Hyperspectral Remote Sensing of Vegetation written by Prasad S. Thenkabail and published by CRC Press. This book was released on 2016-04-19 with total page 766 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research. Hyperspectral Remote Sensing of Vegetation integrates this knowledge, guiding readers to harness the capabilities of the most recent advances in applying hyperspectral remote sensing technology to the study of terrestrial vegetation. Taking a practical approach to a complex subject, the book demonstrates the experience, utility, methods and models used in studying vegetation using hyperspectral data. Written by leading experts, including pioneers in the field, each chapter presents specific applications, reviews existing state-of-the-art knowledge, highlights the advances made, and provides guidance for the appropriate use of hyperspectral data in the study of vegetation as well as its numerous applications, such as crop yield modeling, crop and vegetation biophysical and biochemical property characterization, and crop moisture assessment. This comprehensive book brings together the best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, vegetation classification, biophysical and biochemical modeling, crop productivity and water productivity mapping, and modeling. It provides the pertinent facts, synthesizing findings so that readers can get the correct picture on issues such as the best wavebands for their practical applications, methods of analysis using whole spectra, hyperspectral vegetation indices targeted to study specific biophysical and biochemical quantities, and methods for detecting parameters such as crop moisture variability, chlorophyll content, and stress levels. A collective "knowledge bank," it guides professionals to adopt the best practices for their own work.

Download or read book Comprehensive Remote Sensing written by Shunlin Liang and published by Elsevier. This book was released on 2017-11-08 with total page 3183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Remote Sensing, Nine Volume Set covers all aspects of the topic, with each volume edited by well-known scientists and contributed to by frontier researchers. It is a comprehensive resource that will benefit both students and researchers who want to further their understanding in this discipline. The field of remote sensing has quadrupled in size in the past two decades, and increasingly draws in individuals working in a diverse set of disciplines ranging from geographers, oceanographers, and meteorologists, to physicists and computer scientists. Researchers from a variety of backgrounds are now accessing remote sensing data, creating an urgent need for a one-stop reference work that can comprehensively document the development of remote sensing, from the basic principles, modeling and practical algorithms, to various applications. Fully comprehensive coverage of this rapidly growing discipline, giving readers a detailed overview of all aspects of Remote Sensing principles and applications Contains ‘Layered content’, with each article beginning with the basics and then moving on to more complex concepts Ideal for advanced undergraduates and academic researchers Includes case studies that illustrate the practical application of remote sensing principles, further enhancing understanding

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.