Spatial big data science classification techniques for Earth observation imagery /
Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristi...
| Main Author: | Jiang, Zhe. |
|---|---|
| Other Authors: | Shekhar, Shashi, 1963-, SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
2017.
Cham : 2017. |
| Physical Description: |
1 online resource. |
| Subjects: |
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| 100 | 1 | |a Jiang, Zhe. | |
| 245 | 1 | 0 | |a Spatial big data science : |b classification techniques for Earth observation imagery / |c Zhe Jiang, Shashi Shekhar. |
| 260 | |a Cham : |b Springer, |c 2017. | ||
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| 505 | 0 | |6 880-01 |a Preface; Acknowledgements; Contents; Acronyms; Part I Overview of Spatial Big Data Science; 1 Spatial Big Data; 1.1 What Is Spatial Big Data?; 1.2 Societal Applications; 1.3 Challenges; 1.3.1 Implicit Spatial Relationships; 1.3.2 Spatial Autocorrelation; 1.3.3 Spatial Anisotropy; 1.3.4 Spatial Heterogeneity; 1.3.5 Multiple Scales and Resolutions; 1.4 Organization of the Book; References; 2 Spatial and Spatiotemporal Big Data Science; 2.1 Input: Spatial and Spatiotemporal Data; 2.1.1 Types of Spatial and Spatiotemporal Data; 2.1.2 Data Attributes and Relationships; 2.2 Statistical Foundations. | |
| 505 | 8 | |a 2.2.1 Spatial Statistics for Different Types of Spatial Data2.2.2 Spatiotemporal Statistics; 2.3 Output Pattern Families; 2.3.1 Spatial and Spatiotemporal Outlier Detection; 2.3.2 Spatial and Spatiotemporal Associations, Tele-Connections; 2.3.3 Spatial and Spatiotemporal Prediction; 2.3.4 Spatial and Spatiotemporal Partitioning (Clustering) and Summarization; 2.3.5 Spatial and Spatiotemporal Hotspot Detection; 2.3.6 Spatiotemporal Change; 2.4 Research Trend and Future Research Needs; 2.5 Summary; References; Part II Classification of Earth Observation Imagery Big Data. | |
| 505 | 8 | |a 3 Overview of Earth Imagery Classification3.1 Earth Observation Imagery Big Data; 3.2 Societal Applications; 3.3 Earth Imagery Classification Algorithms; 3.4 Generating Derived Features (Indices); 3.5 Remaining Computational Challenges; References; 4 Spatial Information Gain-Based Spatial Decision Tree; 4.1 Introduction; 4.1.1 Societal Application; 4.1.2 Challenges; 4.1.3 Related Work Summary; 4.2 Problem Formulation; 4.3 Proposed Approach; 4.3.1 Basic Concepts; 4.3.2 Spatial Decision Tree Learning Algorithm; 4.3.3 An Example Execution Trace; 4.4 Evaluation; 4.4.1 Dataset and Settings. | |
| 505 | 8 | |a 5.4.2 A Refined Algorithm5.4.3 Theoretical Analysis; 5.5 Experimental Evaluation; 5.5.1 Experiment Setup; 5.5.2 Classification Performance; 5.5.3 Computational Performance ; 5.6 Discussion; 5.7 Summary; References; 6 Spatial Ensemble Learning; 6.1 Introduction; 6.2 Problem Statement; 6.2.1 Basic Concepts; 6.2.2 Problem Definition; 6.3 Proposed Approach; 6.3.1 Preprocessing: Homogeneous Patches; 6.3.2 Approximate Per Zone Class Ambiguity; 6.3.3 Group Homogeneous Patches into Zones; 6.3.4 Theoretical Analysis; 6.4 Experimental Evaluation; 6.4.1 Experiment Setup. | |
| 520 | |a Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference. | ||
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| 880 | 8 | |6 505-01/(S |a 4.4.2 Does Incorporating Spatial Autocorrelation Improve Classification Accuracy4.4.3 Does Incorporating Spatial Autocorrelation Reduce Salt-and-Pepper Noise; 4.4.4 How May One Choose Ü, the Balancing Parameter for SIG Interestingness Measure; 4.5 Summary; References; 5 Focal-Test-Based Spatial Decision Tree; 5.1 Introduction; 5.2 Basic Concepts and Problem Formulation; 5.2.1 Basic Concepts; 5.2.2 Problem Definition; 5.3 FTSDT Learning Algorithms; 5.3.1 Training Phase; 5.3.2 Prediction Phase; 5.4 Computational Optimization: A Refined Algorithm; 5.4.1 Computational Bottleneck Analysis. | |
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