- Hauptmenü-

Katalogdatenanzeige

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

Kataloginformation Katalogdatensatz500171646 .

Kataloginformation
Feldname	Details
Vorliegende Sprache	eng
ISBN	978-94-007-4074-7
Name	Lakshmanan, Valliappa
T I T E L	Automating the Analysis of Spatial Grids
Zusatz zum Titel	A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications
Verlagsort	Dordrecht
Verlag	Springer Netherlands
Erscheinungsjahr	2012
Erscheinungsjahr	2012
Umfang	Online-Ressource (X, 320 p. 136 illus. in color, digital)
Reihe	Geotechnologies and the Environment ; 6
Notiz / Fußnoten	Description based upon print version of record
Weiterer Inhalt	Automating the Analysis of Spatial Grids; Preface; Contents; Chapter 1: Automated Analysis of Spatial Grids: Motivation and Challenges; 1.1 Geographic Information Systems; 1.2 GIS Operations; 1.3 Need for Automation; 1.4 Spatial Grids; 1.5 Challenges in Automated Analysis; 1.6 Spatial Data Mining Algorithms; 1.6.1 Automatic Land-Type Classification; 1.6.2 Disaster Assessment; 1.6.3 Discovering New Climate Indices; 1.6.4 Change in Forest Cover; 1.6.5 Malaria Eradication; 1.6.6 Monitoring Ecosystem Condition; Chapter 2: Geospatial Grids; 2.1 Representation; 2.1.1 Georeference. 2.1.2 Map Projections2.1.3 Going from One Projection to Another; 2.2 Linearity of Data Values; 2.2.1 Perceptual Color Maps; 2.2.2 Verifying Linearity; 2.3 Instrument Geometry; 2.4 Gridding Point Observations; 2.4.1 Objective Analysis; 2.4.2 Cressman; 2.4.3 Optimization; 2.4.4 Successive Iteration; 2.4.5 Kriging; 2.5 Rasterization; 2.5.1 Points; 2.5.2 Lines; 2.5.3 Splines; 2.5.4 Polygons; 2.5.5 Geocoding Polygons; 2.6 Example Applications; Chapter 3: Data Structures for Spatial Grids; 3.1 Array; 3.2 Pixels; 3.3 Level Set; 3.4 Topographical Surface; 3.5 Markov Chain; 3.6 Matrix. 3.7 Parametric Approximation3.8 Relational Structure; 3.9 Applications; Chapter 4: Global and Local Image Statistics; 4.1 Types of Statistics; 4.2 Distances; 4.2.1 Pixel to Cluster; 4.2.2 Cluster to Cluster; 4.3 Distance Transform; 4.3.1 Ordered Propagation; 4.3.2 Saiko and Toriwaki Algorithm; 4.3.3 Geodesic Distance; 4.4 Probability Functions; 4.4.1 Shannon Entropy; 4.4.2 Kolmogorov-Smirnov Test; 4.4.3 Threshold Selection; 4.5 Local Measures; 4.5.1 Quantization; 4.5.1.1 Histogram Equalization; 4.5.1.2 Vector Quantization; 4.6 Example Applications. Chapter 5: Neighborhood and Window Operations5.1 Preprocessing; 5.2 Window Operations; 5.2.1 Smoothing; 5.2.2 Matched Filter; 5.2.3 Directional Smoothing; 5.2.4 Filter Bank; 5.2.5 Separability; 5.2.6 Edge Detection; 5.3 Median Filter; 5.3.1 Speckle Filtering; 5.4 Morphological Operations; 5.5 Skeletonization; 5.5.1 Thinning; 5.6 Frequency-Domain Convolution; 5.7 Example Applications; Chapter 6: Identifying Objects; 6.1 Object Identification; 6.2 Region Growing; 6.3 Region Properties; 6.3.1 Size; 6.3.2 Geocoding Objects; 6.3.3 Orientation and Aspect Ratio; 6.3.4 Fitting Lines; 6.4 Hysteresis. 6.5 Active Contours6.6 Watershed Transform; 6.7 Enhanced Watershed; 6.8 Contiguity-Enhanced Clustering; 6.9 Choosing an Object Identification Technique; 6.10 Example Applications; Chapter 7: Change and Motion Estimation; 7.1 Estimating Change; 7.2 Optical Flow; 7.2.1 Partial Derivatives; 7.2.2 Cross-correlation; 7.2.3 Pyramidal Cross-correlation; 7.2.4 Phase Correlation; 7.3 Object-Tracking; 7.3.1 Hungarian Method; 7.3.2 Kalman Filter; 7.3.3 Hybrid Techniques; 7.3.4 Interpolating a Motion Field; 7.4 Choosing a Change or Motion Estimation Technique; 7.4.1 Temporal Attributes. 7.5 Example Applications
Titelhinweis	Buchausg. u.d.T.ISBN: 978-94-007-4074-7
ISBN	ISBN 978-94-007-4075-4
	ISBN 1-280-99641-2 ebk
	ISBN 978-1-280-99641-2 MyiLibrary
Klassifikation	RB
	SCI019000
	624.151
	910.285
	TA703-705.4
	RB 10103
	RB 10104
	RB 10208
Kurzbeschreibung	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.
1. Schlagwortkette	Geoinformationssystem
	Data Mining
	Raumgitter
	Anthropogeografie
	Umweltgeografie
ANZEIGE DER KETTE	Geoinformationssystem -- Data Mining -- Raumgitter -- Anthropogeografie -- Umweltgeografie
SWB-Titel-Idn	367687003
Signatur	Springer E-Book
Bemerkungen	Elektronischer Volltext - Campuslizenz
Elektronische Adresse	$uhttp://dx.doi.org/10.1007/978-94-007-4075-4
Internetseite / Link	Volltext
Siehe auch	Volltext
Siehe auch	Cover

ISBD-Anzeige Katalogdatensatz500171646

Kategorien-Anzeige Katalogdatensatz500171646

Titel zur Titelsammlung hinzufügen Katalogdatensatz500171646

Kataloginformation500171646 Datensatzanfang . Kataloginformation500171646 Seitenanfang .

Titel vormerken Katalogdatensatz500171646

Vollanzeige Katalogdaten

Auf diesem Bildschirm erhalten Sie Katalog- und Exemplarinformationen zum ausgewählten Titel.

Im Bereich Kataloginformation werden die bibliographischen Details angezeigt. Per Klick auf Hyperlink-Begriffe wie Schlagwörter, Autoren, Reihen, Körperschaften und Klassifikationen können Sie sich weitere Titel des gewählten Begriffes anzeigen lassen.

Der Bereich Exemplarinformationen enthält zum einen Angaben über den Standort und die Verfügbarkeit der Exemplare. Zum anderen haben Sie die Möglichkeit, ausgeliehene Exemplare vorzumerken oder Exemplare aus dem Magazin zu bestellen.