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Object-Based Image Analysis Spatial Concepts for Knowledge-Driven Remote Sensing Applications

:	William Cartwright, Georg Gartner, Liqiu Meng, Michael P. Peterson, Thomas Blaschke, Stefan Lang, Ge
:	Thomas Blaschke, Stefan Lang, Geoffrey Hay
:	Object-Based Image Analysis Spatial Concepts for Knowledge-Driven Remote Sensing Applications
:	Springer-Verlag
:	9783540770589
:	1
:	CHF 209.00
:

:	Geografie
:	English

:	817
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed 'obje- oriented image analysis'. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).

	Preface	5
	Acknowledgements	8
	Contents	9
	External Reviewers	15
	Section 1 Why object-based image analysis	18
	Chapter 1.1 Object-based image analysis for remote sensing applications: modeling reality – dealing with complexity	19
	1 Monitoring needs in a dynamic world	20
	2 A plurality of solutions – conditioned information and geons	24
	3 Class modeling	27
	4 Object assessment and evaluation	34
	6 Conclusion	40
	Acknowledgements	40
	References	40
	Chapter 1.2 Progressing from object-based to object-oriented image analysis	44
	1 Introduction	44
	2 Methodology	46
	3 Case study – single tree detection	50
	4 Discussion	56
	5 References	56
	Chapter 1.3 An object-based cellular automata model to mitigate scale dependency	58
	1 Introduction	59
	2 Scale dependency in spatial analysis and modeling	60
	3 The Vector-based Geographic Cellular Automata Model (VecGCA)	67
	4. Conclusion	80
	Acknowledgements	81
	References	81
	Chapter 1.4 Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline	89
	1 Introduction	89
	2 What is GEOBIA? A definition	91
	3 Why GEOBIA instead of OBIA?	92
	4 GEOBIA: A key objective	93
	5 Why is GEOBIA?	94
	6 GEOBIA SWOT	95
	7 GEOBIA Tenets	100
	8. Conclusion	101
	Acknowledgements	102
	References	102
	Chapter 1.5 Image objects and geographic objects	104
	1 Introduction	104
	2 Image-objects	107
	3 Geo-objects	111
	4 Linking image-objects to geo-objects	117
	4.1 Meaningful image-objects	118
	4.2. Object-based classification	119
	5 Summary	121
	Acknowledgements	121
	References	121
	Section 2 Multiscale representation and object-based classification	124
	Chapter 2.1 Using texture to tackle the problem of scale in land-cover classification	125
	1 Introduction	125
	2 A conceptual model of aerial photo interpretation	127
	3 Methodology	130
	4 Conclusions and Future Work	142
	References	143
	Chapter 2.2 Domain-specific class modelling for one-level representation of single trees	145
	1 Introduction	146
	2 Study Areas and Data sets	147
	3 Methodology	149
	4 Results and Discussion	155
	5 Conclusions	159
	References	160
	Acknowledgments	163
	Chapter 2.3 Object recognition and image segmentation: the Feature Analyst® approach	164
	1 Introduction	165
	2 Learning Applied to Image Analysis	166
	3 Feature Analyst	167
	5 Conclusions	175
	References	177
	Chapter 2.4 A procedure for automatic object-based classification	179
	1 Introduction	180
	2 Theoretical background	181
	3 Towards automation	183
	4 Case studies	186
	5. CONCLUSION	193
	References	194
	Chapter 2.5 Change detection using object features	195
	1 Introduction	195
	2 Methodology	198
	3 Case study	204
	4. Conclusions and future work	209
	References	210
	Chapter 2.6 Identifying benefits of pre-processing large area QuickBird imagery for object-based image analysis	212
	1 Introduction	213
	2 The pre-processing applied	214
	3 Benefits of pre-processing	215
	4 Deriving landscape patterns in the agricultural matrix	217
	5 Summary and outlook	220
	Note	221
	References	221
	Chapter 2.7 A hybrid texture-based and region-based multiscale image segmentation algorithm	229
	1 Introduction	230
	2 Methodology	232
	3 Discussion of Results	237
	4 Conclusions and future work	242
	Acknowledgements	243
	References	243
	Chapter 2.8 Semi-automated forest stand delineation using wavelet based segmentation of very high resolution optical imagery	245
	1 Introduction	246
	2 Artificial imagery	246
	3 Wavelets transforms	249
	4 Materials	250
	5 Method	251
	6 Results and discussion	255
	7 Conclusion	261
	Acknowledgements	262
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