Automating the Analysis of Spatial Grids: A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications - Tapa blanda

Sinopsis

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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