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Author: Rex Peacock Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 48
Book Description
Remotely sensed data is an important component of land use/land cover (LULC) studies. This research utilized the vegetation-impervious surface-soil (V-I-S) model. Using Enhanced Thematic Mapper Plus (ETM+) imagery, this research compared the accuracy of supervised and unsupervised classification by analyzing three study areas in and near Little Rock, Arkansas. The first study area was a homogeneous region comprised primarily of water features. The second study area was a region of an intermediate mix of land cover classes. The third study area was a region of heterogeneous land cover composition between the four land cover classes of the V-I-S model. Upon the completion of supervised and unsupervised classification, 200 points for each area were randomly generated using a stratified random sampling approach. The land cover data associated with these points were then compared to ground truth data derived from higher-resolution imagery from the National Agriculture Imagery Program (NAIP). Based on error matrices, the homogeneous and intermediate study areas featured higher accuracy values for unsupervised classification over supervised classification. For the heterogeneous study area, supervised classification was more accurate than unsupervised classification by one percent.
Author: Rex Peacock Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 48
Book Description
Remotely sensed data is an important component of land use/land cover (LULC) studies. This research utilized the vegetation-impervious surface-soil (V-I-S) model. Using Enhanced Thematic Mapper Plus (ETM+) imagery, this research compared the accuracy of supervised and unsupervised classification by analyzing three study areas in and near Little Rock, Arkansas. The first study area was a homogeneous region comprised primarily of water features. The second study area was a region of an intermediate mix of land cover classes. The third study area was a region of heterogeneous land cover composition between the four land cover classes of the V-I-S model. Upon the completion of supervised and unsupervised classification, 200 points for each area were randomly generated using a stratified random sampling approach. The land cover data associated with these points were then compared to ground truth data derived from higher-resolution imagery from the National Agriculture Imagery Program (NAIP). Based on error matrices, the homogeneous and intermediate study areas featured higher accuracy values for unsupervised classification over supervised classification. For the heterogeneous study area, supervised classification was more accurate than unsupervised classification by one percent.
Author: Taskin Kavzoglu Publisher: CRC Press ISBN: 104009905X Category : Technology & Engineering Languages : en Pages : 444
Book Description
The third edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data. This book is thoroughly updated to meet the needs of readers today and provides six new chapters on deep learning, feature extraction and selection, multisource image fusion, hyperparameter optimization, accuracy assessment with model explainability, and object-based image analysis, which is relatively a new paradigm in image processing and classification. It presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. New in this edition: Provides comprehensive background on the theory of deep learning and its application to remote sensing data. Includes a chapter on hyperparameter optimization techniques to guarantee the highest performance in classification applications. Outlines the latest strategies and accuracy measures in accuracy assessment and summarizes accuracy metrics and assessment strategies. Discusses the methods used for explaining inherent structures and weighing the features of ML and AI algorithms that are critical for explaining the robustness of the models. This book is intended for industry professionals, researchers, academics, and graduate students who want a thorough and up-to-date guide to the many and varied techniques of image classification applied in the fields of geography, geospatial and earth sciences, electronic and computer science, environmental engineering, etc.
Author: Anil Kumar Publisher: CRC Press ISBN: 1000091546 Category : Computers Languages : en Pages : 177
Book Description
This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.
Author: Giles M. Foody Publisher: John Wiley & Sons ISBN: 0470859245 Category : Technology & Engineering Languages : en Pages : 326
Book Description
Remote sensing and geographical information science (GIS) have advanced considerably in recent years. However, the potential of remote sensing and GIS within the environmental sciences is limited by uncertainty, especially in connection with the data sets and methods used. In many studies, the issue of uncertainty has been incompletely addressed. The situation has arisen in part from a lack of appreciation of uncertainty and the problems it can cause as well as of the techniques that may be used to accommodate it. This book provides general overviews on uncertainty in remote sensing and GIS that illustrate the range of uncertainties that may occur, in addition to describing the means of measuring uncertainty and the impacts of uncertainty on analyses and interpretations made. Uncertainty in Remote Sensing and GIS provides readers with comprehensive coverage of this largely undocumented subject: * Relevant to a broad variety of disciplines including geography, environmental science, electrical engineering and statistics * Covers range of material from base overviews to specific applications * Focuses on issues connected with uncertainty at various points along typical data analysis chains used in remote sensing and GIS Written by an international team of researchers drawn from a variety of disciplines, Uncertainty in Remote Sensing and GIS provides focussed discussions on topics of considerable importance to a broad research and user community. The book is invaluable reading for researchers, advanced students and practitioners who want to understand the nature of uncertainty in remote sensing and GIS, its limitations and methods of accommodating it.
Author: Mohammad Tanveer Alam Publisher: ISBN: Category : Classification Languages : en Pages : 114
Book Description
Remote Sensing engages electromagnetic sensors to measure and monitor changes in the earth's surface and atmosphere. Remote Sensing Satellites are currently the fastest growing source of geographical area. Using data-mining techniques enables more opportunistic use of data banks of remote sensing satellite images. This thesis focuses on supervised and unsupervised classification, the two data mining techniques on the high resolution satellite Imagery from satellite IKONOS and satellite LANDSAT taken of the area around Kent State University, Ohio. The image was classified into ten distinct class: 1) Water, 2) Forested, 3) Agriculture, 4) Urban Development, 5) Vegetation1, 6) Vegetation2, 7) Vegetation3, 8) Vegetation4, 9) Grass, 10)Road. ERDAS Imagine was used in manipulating the images and creating the classification and analysis. The result obtained in form of accuracy helps to decide which image and classification technique is better to identify geographical patterns related to land use.
Author: Rex Peacock
Author: Rex Peacock
Author: James A. King
Author: Ruth A. Norman
Author: Taskin Kavzoglu
Author: Anil Kumar
Author: Nagarajan Sureshkumar
Author: Ramiro Salcedo
Author: Giles M. Foody
Author: Mohammad Tanveer Alam
Author: Venu G. Oddiraju