Automating the Analysis of Spatial Grids A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications /

The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and...

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Lakshmanan, Valliappa (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:Inglés
Published: Dordrecht : Springer Netherlands : Imprint: Springer, 2012.
Edition:1st ed. 2012.
Series:Geotechnologies and the Environment, 6
Subjects:
Geotechnical engineering.
Geographic information systems.
Data mining.
Earth sciences.
Geotechnical Engineering and Applied Earth Sciences.
Geographical Information System.
Data Mining and Knowledge Discovery.
Earth Sciences.
Online Access:Texto Completo
Table of Contents:
  • Automated Analysis of Spatial Grids: Motivation and Challenges
  • -Geographic Information Systems
  • -GIS Operations
  • -Need for Automation
  • -Spatial Grids
  • -Challenges in Automated Analysis
  • -Spatial Data Mining Algorithms
  • Geospatial grids
  • -Representation
  • -Linearity of data values
  • -Instrument geometry
  • -Gridding point observations
  • -Rasterization
  • -Example Applications
  • Data Structures for Spatial Grids
  • -Array
  • -Pixels
  • -Level set
  • -Topographical surface
  • -Markov chain
  • -Matrix
  • -Parametric approximation
  • -Relational structure
  • -Applications
  • Global and Local Image Statistics
  • -Types of statistics
  • -Distances
  • -Distance transform
  • -Probability Functions
  • -Local measures
  • -Example Applications
  • Neighborhood and Window Operations
  • -Preprocessing
  • -Window operations
  • -Median filter
  • -Morphological operations
  • -Skeletonization
  • -Frequency Domain Convolution
  • -Example Applications
  • Identifying Objects
  • -Object identification
  • -Region growing
  • -Region properties
  • -Hysteresis
  • -Active contours
  • -Watershed Transform
  • -Enhanced watershed
  • -Contiguity-enhanced Clustering
  • -Choosing an object-identification technique
  • -Example Applications
  • Change and Motion Estimation
  • -Estimating change
  • -Optical Flow
  • -Object-tracking
  • -Choosing a change or motion estimation technique
  • -Example Applications
  • Data Mining Attributes from Spatial Grids
  • -Data Mining
  • -A Fuzzy Logic Application
  • -Supervised learning models
  • -Clustering
  • -Example Applications.

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