Radar And Multispectral Image Fusion Options For Improved Land Cover Classification
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Radar And Multispectral Image Fusion Options For Improved Land Cover Classification
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Author : Erwin J. Villiger
language : en
Publisher:
Release Date : 2008
Radar And Multispectral Image Fusion Options For Improved Land Cover Classification written by Erwin J. Villiger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Image converters categories.
Investigators engaged in research utilizing remotely-sensed data are increasingly faced with a plethora of data sources and platforms that exploit different portions of the electromagnetic spectrum. Considerable efforts have focused on the application of these sources to the development of a better understanding of lithosphere, biosphere, and atmospheric systems. Many of these efforts have concentrated on the use of single sensors. More recently, some research efforts have turned to the fusion of sources in an effort to determine if different sensors and platforms can be combined to more effectively analyze or model the systems in question. This study evaluates multisensor integration of Synthetic Aperture Radar (SAR) with Multispectral Imagery (MSI) data for land cover analysis and vegetation mapping. Three principle analytical issues are addressed in this investigation: the value of SAR collected at different incident angles, preclassification processing alternatives to improve fusion classification results, and the value of cross-season (dry and wet) data integration in a subtropical climate. The study site for this research is Andros Island, the largest island in The Bahamas archipelago. Andros has a number of distinct plant communities ranging from saltwater marsh and mangroves to pine stands and hardwood coppices. Despite the island's size and proximity to the United States, it is largely uninhabited and has large expanses of minimally disturbed landscapes. An empirical assessment of SAR filtering techniques, namely speckle suppression and texture analysis at various window sizes, is utilized to determine the most appropriate technique to apply when integrating SAR and MSI for land cover characterization. Multiple RADARSAT-1 SAR images were collected at various incident angles for wet and dry season conditions over the region of interest. Two Landsat Thematic Mapper-5 MSI datasets were also collected to coincide with the time periods of the SAR images. A land cover classification process applied to the dry season and wet season MSI data achieved a total classification accuracy of 80.6% and 80.7% respectively. When combined into a single multiseason dataset the MSI data resulted in a total classification accuracy of 87.3%. SAR proved to be a valuable source of information especially when processed as a time series and with a speckle suppression algorithm applied. A 21-scene multitemporal SAR dataset achieved a total classification accuracy of 65.8%. When a classification was applied to the multitemporal dataset following speckle suppression, the resulting total classification accuracy was as high as 83.8% depending on the speckle algorithm and kernel applied. While texture measures have been successfully utilized for integrating SAR and MSI data, in this study speckle suppression proved to be significantly more valuable. SAR collection parameters such as look direction (ascending or descending orbit) and incident angle did not prove to contain uniquely valuable characteristics. The highest total classification accuracy achieved involved a combination of two MSI datasets and a multitemporal SAR dataset processed to suppress speckle using a Gamma- Maximum A Posteriori (MAP) filter with a 9x9 kernel. This study sought to investigate processing alternatives when fusing SAR and MSI data. While not all of the results met with expectations, this study does determine that SAR and MSI are complementary data sources. A combination of SAR and MSI provide unique and valuable results that can not be achieved by each variable used independently.
Dissertation Abstracts International
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Author :
language : en
Publisher:
Release Date : 2008
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Dissertations, Academic categories.
Land Cover Use Classification Using Optical And Quad Polarization Radar Imagery
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Author : Arjun Sheoran
language : en
Publisher:
Release Date : 2009
Land Cover Use Classification Using Optical And Quad Polarization Radar Imagery written by Arjun Sheoran and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Imaging systems categories.
With the recent increase in the availability of quad polarization (Horizontal-Horizontal, Horizontal-Vertical, Vertical-Horizontal and Vertical-Vertical) radar data, the need to assess the utility of these datasets for land cover/use classification is crucial. Historically, most spaceborne radars were single wavelength and single polarization. For this study, the Japanese ALOS (Advanced Land Observing Satellite) PALSAR (Phased type L-band Synthetic Aperture Radar) quad polarization radar data were obtained at 12.5 meter spatial resolution. The second dataset to be used in this study was acquired by Landsat TM (Thematic Mapper) at a 28.5 meter spatial resolution. The purpose of this study is to evaluate the classification of various land covers/uses using spaceborne quad polarization radar and optical TM data. Secondly, the study analyzes the utility and improvements that can be made to the radar and TM data with the help of using radar texture and multi sensor fusion techniques, e.g., layer stacking and Principal Component Analysis (PCA). Three study sites Bangladesh, California and Kenya were chosen for analysis in this study. The primary methodology was spectral signature extraction and Transformed Divergence (TD) separability measures to evaluate the relative utility of the various data types. In addition four texture measures, kurtosis, mean euclidean distance, skewness, and variance and four window sizes were analyzed. Supervised signature extraction and classification (maximum likelihood) was used to classify different land covers/uses followed by an accuracy assessment. The combination of radar and Landsat consistently provided excellent classification accuracies, well over 90%. Comparing the two datasets Landsat provided higher classification accuracies as compared to radar and radar texture analyzed individually. Variance texture was consistently the best among all four texture measures, as it showed the most improvement in the TD values. The use of texture on radar was helpful when evaluating the separability among the different land covers/uses. However, texture was not able to provide higher classification accuracies for the different land covers/uses as compared to the original radar and Landsat datasets.
Sar Image Interpretation For Various Land Covers
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Author : Elizabeth L. Simms
language : en
Publisher: CRC Press
Release Date : 2019-12-06
Sar Image Interpretation For Various Land Covers written by Elizabeth L. Simms and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-06 with Science categories.
This full color book is a comprehensive visual reference for the interpretation of synthetic aperture radar (SAR) images with examples of how technological specifications may affect interpretation solutions. It contains a summary review of image acquisition parameters of consequence on the visual representation of objects, introduces traditional interpretation keys under different light and applies them for considering regional landscape components and identifying large-scale geographical ensembles. Through elements of interpretation such as the construct of tone, texture, pattern, size, and shape, the book explains the rich unique context of many terrains. It provides also several SAR X- and C-band image examples of regional and large-scale land use and land cover (LULC) ensembles, includes important explanations for each illustration, and highlights selected SAR image applications. Ancillary information includes acquisition specifications, a geographic scale, and the image-center latitude and longitude. Features: Provides ready access to any type of information for an image interpretation problem related to current LULC classification schemes. Presents scalable geographic information interpreted at a regional scale and land cover ensembles that can also be interpreted locally. Provides comparative examples of images acquired from X- and C-band, opposed look directions, near- and far-range incidence angles, like- and cross-polarization modes. Includes practical explanations easily transferred to individual’s research projects. Designed as "visual dictionary," SAR Image Interpretation for Various Land Covers: A Practical Guide, is an excellent introduction to the visual interpretation of SAR images for numerous types of LULC. Both practitioners and students will familiarize themselves with and expand their knowledge of geographic information conveyed from radar images while government agencies and businesses that use LULC-related data for emergency response cases of for urban and regional planning, will find this book invaluable.
Guide To Programs Of Geography In The United States And Canada
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Author :
language : en
Publisher:
Release Date : 2008
Guide To Programs Of Geography In The United States And Canada written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Geography categories.
Land Applications Of Radar Remote Sensing
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Author : Damien Closson
language : en
Publisher: BoD – Books on Demand
Release Date : 2014-06-11
Land Applications Of Radar Remote Sensing written by Damien Closson and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-11 with Science categories.
The aim of this book is to demonstrate the use of SAR data in three application domains, i.e. land cover (Part II), topography (Part III), and land motion (Part IV). These are preceded by Part I, where an extensive and complete review on speckle and adaptive filtering is provided, essential for the understanding of SAR images. Part II is dedicated to land cover mapping. Part III is devoted to the generation of Digital Elevation Models based on radargrammetry and on a wise fusion (by considering sensor characteristic and acquisition geometry) of interferometric and photogrammetric elevation data. Part IV provides a contribution to three applications related to land motion.
Analysis Of Spaceborne Synthetic Aperture Radar Images To Assist In Statewide Land Cover Mapping And Long Term Ecological Research
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Author : Jonathan Ward Chipman
language : en
Publisher:
Release Date : 1996
Analysis Of Spaceborne Synthetic Aperture Radar Images To Assist In Statewide Land Cover Mapping And Long Term Ecological Research written by Jonathan Ward Chipman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with categories.
Land Cover Classification Of Remotely Sensed Images
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Author : S. Jenicka
language : en
Publisher: Springer Nature
Release Date : 2021-03-10
Land Cover Classification Of Remotely Sensed Images written by S. Jenicka and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-10 with Technology & Engineering categories.
The book introduces two domains namely Remote Sensing and Digital Image Processing. It discusses remote sensing, texture, classifiers, and procedures for performing the texture-based segmentation and land cover classification. The first chapter discusses the important terminologies in remote sensing, basics of land cover classification, types of remotely sensed images and their characteristics. The second chapter introduces the texture and a detailed literature survey citing papers related to texture analysis and image processing. The third chapter describes basic texture models for gray level images and multivariate texture models for color or remotely sensed images with relevant Matlab source codes. The fourth chapter focuses on texture-based classification and texture-based segmentation. The Matlab source codes for performing supervised texture based segmentation using basic texture models and minimum distance classifier are listed. The fifth chapter describes supervised and unsupervised classifiers. The experimental results obtained using a basic texture model (Uniform Local Binary Pattern) with the classifiers described earlier are discussed through the relevant Matlab source codes. The sixth chapter describes land cover classification procedure using multivariate (statistical and spectral) texture models and minimum distance classifier with Matlab source codes. A few performance metrics are also explained. The seventh chapter explains how texture based segmentation and land cover classification are performed using the hidden Markov model with relevant Matlab source codes. The eighth chapter gives an overview of spatial data analysis and other existing land cover classification methods. The ninth chapter addresses the research issues and challenges associated with land cover classification using textural approaches. This book is useful for undergraduates in Computer Science and Civil Engineering and postgraduates who plan to do research or project work in digital image processing. The book can serve as a guide to those who narrow down their research to processing remotely sensed images. It addresses a wide range of texture models and classifiers. The book not only guides but aids the reader in implementing the concepts through the Matlab source codes listed. In short, the book will be a valuable resource for growing academicians to gain expertise in their area of specialization and students who aim at gaining in-depth knowledge through practical implementations. The exercises given under texture based segmentation (excluding land cover classification exercises) can serve as lab exercises for the undergraduate students who learn texture based image processing.
Remote Sensing Image Fusion
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Author : Luciano Alparone
language : en
Publisher: CRC Press
Release Date : 2015-03-06
Remote Sensing Image Fusion written by Luciano Alparone and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-06 with Science categories.
A synthesis of more than ten years of experience, Remote Sensing Image Fusion covers methods specifically designed for remote sensing imagery. The authors supply a comprehensive classification system and rigorous mathematical description of advanced and state-of-the-art methods for pansharpening of multispectral images, fusion of hyperspectral and
Handbook Of Neural Computation
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Author : Pijush Samui
language : en
Publisher: Academic Press
Release Date : 2017-07-18
Handbook Of Neural Computation written by Pijush Samui and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-18 with Technology & Engineering categories.
Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods