Automating the Analysis of Spatial Grids
A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications
Published on 14. June 2012
Book
Hardback
X, 320 pages
978-94-007-4074-7 (ISBN)
Description
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 detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. The aim is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution survey imagery.
More details
Series
Edition
2012 ed.
Language
English
Place of publication
Dordrecht
Netherlands
Target group
Primary & secondary/elementary & high school
Graduate
Illustrations
X, 320 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 21 mm
Weight
724 gr
ISBN-13
978-94-007-4074-7 (9789400740747)
DOI
10.1007/978-94-007-4075-4
Schweitzer Classification
Other editions
Additional editions
Valliappa Lakshmanan
Automating the Analysis of Spatial Grids
A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications
Book
08/2016
Springer
€160.49
Shipment within 15-20 days
Valliappa Lakshmanan
Automating the Analysis of Spatial Grids
A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications
E-Book
06/2012
Springer
€149.79
Available for download
Person
Dr. Valliappa Lakshmanan is an expert in machine intelligence R&D for meteorological applications, and in designing and developing large-scale software systems. He is skilled in communicating technical and non-technical material to diverse audiences. He has studied at the Indian Institute of Technology in Madras, the Ohio State University in Columbus and the University of Oklahoma.
Dr. Lakshmanan is currently employed as a Research Scientist at CIMMS, being the technical lead on several software projects and research groups.
He also develops automated real-time pattern recognition algorithms and visualization techniques for weather phenomena.
He has (co-)written many journal articles.
Dr. Lakshmanan is currently employed as a Research Scientist at CIMMS, being the technical lead on several software projects and research groups.
He also develops automated real-time pattern recognition algorithms and visualization techniques for weather phenomena.
He has (co-)written many journal articles.
Content
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.
-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.