Pattern Recognition Methods For Crop Classification From Hyperspectral Remote Sensing Images
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Pattern Recognition Methods For Crop Classification From Hyperspectral Remote Sensing Images
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Author : Luis Gomez Chova
language : en
Publisher: Universal-Publishers
Release Date : 2004-05-21
Pattern Recognition Methods For Crop Classification From Hyperspectral Remote Sensing Images written by Luis Gomez Chova and has been published by Universal-Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-21 with Science categories.
(Complete work in Spanish) Remote sensing aerial spectral imaging was one of the first application areas where spectral imaging was used in order to identify and monitor the natural resources and covers on earth surface. Aerial spectral imaging is being developed with the aim of monitoring natural resources like coastal areas, forestry and extensive crops. The information contained in hyperspectral images allows the reconstruction of the energy curve radiated by the terrestrial surface throughout the electromagnetic spectrum. Hence, the characterization, identification and classification of the observed material from their spectral curve is an interesting possibility. Pattern recognition methods have proven to be effective techniques in this kind of applications. In fact, classification of surface features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Nevertheless, the main problem with supervised methods is that the learning process heavily depends on the quality of the training data set and the input space dimensionality. Certainly, these are main issues to be addressed, given the high cost of true sample labeling, the high number of spectral bands, and the high variability of the earth surface and the illumination conditions. In practice, a preprocessing stage (feature selection/extraction) is time-consuming, scenario-dependent and needs a priori knowledge. Thus, more efforts must be done to improve classification methods, in terms of accuracy, robustness, reliability, real-time performance and interpretability. This work is a contribution to the Digital Airborne Imaging Spectrometer Experiment (DAISEX) project, funded by the European Space Agency (ESA) within the framework of its Earth Observation Preparatory Program during 1998, 1999, and 2000. During the DAISEX campaign, hyperspectral images were acquired with the HyMap spectrometer (128-band scanner with a discontinuous spectral range: 0.4 μm - 2.5 μm). In this context, we have carried out an extensive comparison of state-of-the-art methods to develop crop cover classifiers and to obtain a thematic map of the crops on the scene. On one hand, in order to circumvent problems when dealing with a high dimensional input space (induced by the high resolution of the HyMap spectrometer, 128 bands), we have studied a preprocessing stage of feature selection/extraction. This first stage analyses the most critical spectral bands for the present subject. On the other hand, we have developed many methods, both supervised and unsupervised, for crop cover classification, image clustering, interpretation and robustness tests. All these approaches have been included in a general learning scheme where we propose a combined strategy of supervised and unsupervised learning methods that avoids these drawbacks and automates the classification process. In this study, we review the work carried out in that sense and how it could be useful in further applications.
Pattern Recognition And Image Analysis
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Author : Francisco J. Perales López
language : en
Publisher: Springer
Release Date : 2003-10-02
Pattern Recognition And Image Analysis written by Francisco J. Perales López and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-10-02 with Computers categories.
The refereed proceedings of the First Iberial Conference on Pattern Recognition and Image Analysis, IbPria 2003, held in Puerto de Andratx, Mallorca, Spain in June 2003. The 130 revised papers presented were carefully reviewed and selected from 185 full papers submitted. All current aspects of ongoing research in computer vision, image processing, pattern recognition, and speech recognition are addressed.
Recent Advances In Quantitative Remote Sensing
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Author : José A. Sobrino
language : en
Publisher: Universitat de València
Release Date : 2002
Recent Advances In Quantitative Remote Sensing written by José A. Sobrino and has been published by Universitat de València this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Science categories.
Pattern Recognition
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Author : Shutao Li
language : en
Publisher: Springer
Release Date : 2014-11-05
Pattern Recognition written by Shutao Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-05 with Computers categories.
The two-volume set CCIS 483 and CCIS 484 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition, CCPR 2014, held in Changsha, China, in November 2014. The 112 revised full papers presented in two volumes were carefully reviewed and selected from 225 submissions. The papers are organized in topical sections on fundamentals of pattern recognition; feature extraction and classification; computer vision; image processing and analysis; video processing and analysis; biometric and action recognition; biomedical image analysis; document and speech analysis; pattern recognition applications.
Hyperspectral Remote Sensing Of Vegetation Second Edition Four Volume Set
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Author : Prasad S. Thenkabail
language : en
Publisher: CRC Press
Release Date : 2022-07-30
Hyperspectral Remote Sensing Of Vegetation Second Edition Four Volume Set written by Prasad S. Thenkabail and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-30 with Technology & Engineering categories.
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 Volume II, Hyperspectral Indices and Image Classifications for Agriculture and Vegetation evaluates the performance of hyperspectral narrowband or imaging spectroscopy data with specific emphasis on the uses and applications of hyperspectral narrowband vegetation indices in characterizing, modeling, mapping, and monitoring agricultural crops and vegetation Volume III, Biophysical and Biochemical Characterization and Plant Species Studies demonstrates the methods that are developed and used to study terrestrial vegetation using hyperspectral data. This volume includes extensive discussions on hyperspectral data processing and how to implement data processing mechanisms for specific biophysical and biochemical applications such as crop yield modeling, crop biophysical and biochemical property characterization, and crop moisture assessments Volume IV, Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation discusses the use of hyperspectral or imaging spectroscopy data in numerous specific and advanced applications, such as forest management, precision farming, managing invasive species, and local to global land cover change detection.
State Of The Art Technology And Applications In Crop Phenomics
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Author : Wanneng Yang
language : en
Publisher: Frontiers Media SA
Release Date : 2021-12-01
State Of The Art Technology And Applications In Crop Phenomics written by Wanneng Yang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-01 with Science categories.
Pattern Recognition And Image Analysis
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Author :
language : en
Publisher:
Release Date : 2003
Pattern Recognition And Image Analysis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computer vision categories.
Historical Land Use Land Cover Classification Using Remote Sensing
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Author : Wafi Al-Fares
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-25
Historical Land Use Land Cover Classification Using Remote Sensing written by Wafi Al-Fares and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-25 with Science categories.
Although the development of remote sensing techniques focuses greatly on construction of new sensors with higher spatial and spectral resolution, it is advisable to also use data of older sensors (especially, the LANDSAT-mission) when the historical mapping of land use/land cover and monitoring of their dynamics are needed. Using data from LANDSAT missions as well as from Terra (ASTER) Sensors, the authors shows in his book maps of historical land cover changes with a focus on agricultural irrigation projects. The kernel of this study was whether, how and to what extent applying the various remotely sensed data that were used here, would be an effective approach to classify the historical and current land use/land cover, to monitor the dynamics of land use/land cover during the last four decades, to map the development of the irrigation areas, and to classify the major strategic winter- and summer-irrigated agricultural crops in the study area of the Euphrates River Basin.
Kernel Methods For Remote Sensing Data Analysis
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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-03
Kernel Methods For Remote Sensing Data Analysis written by Gustau Camps-Valls and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-03 with Technology & Engineering categories.
Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.
Hyperspectral Remote Sensing
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Author : Ruiliang Pu
language : en
Publisher: CRC Press
Release Date : 2017-08-16
Hyperspectral Remote Sensing written by Ruiliang Pu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-16 with Science categories.
Advanced imaging spectral technology and hyperspectral analysis techniques for multiple applications are the key features of the book. This book will present in one volume complete solutions from concepts, fundamentals, and methods of acquisition of hyperspectral data to analyses and applications of the data in a very coherent manner. It will help readers to fully understand basic theories of HRS, how to utilize various field spectrometers and bioinstruments, the importance of radiometric correction and atmospheric correction, the use of analysis, tools and software, and determine what to do with HRS technology and data.