Geoffrey J. Hay - Academia.edu (original) (raw)

Papers by Geoffrey J. Hay

This paper deals with the description and assessment of uncertainties in gridded land use data de... more This paper deals with the description and assessment of uncertainties in gridded land use data derived from Remote Sensing observations, in the context of hydrological studies. Land use is a categorical regionalised variable returning the main socioeconomic role each location has, where the role is inferred from the pattern of occupation of land. There are two main uncertainties surrounding land use data, positional and categorical. This paper focuses on the second one, as the first one has in general less serious implications and is easier to tackle. The conventional method used to asess categorical uncertainty, the confusion matrix, is criticised in depth, the main critique being its inability to inform on a basic requirement to propagate uncertainty through distributed hydrological models, namely the spatial distribution of errors. Some existing alternative methods are reported, and finally the need for metadata is stressed as a more reliable means to assess the quality, and hence the uncertainty, of these data.

Springer eBooks, Aug 27, 2008

Editors Prof. Thomas Blaschke Universitat Salzburg Zentrum fur Geoinformatik Hellbrunner Str. 34 ... more Editors Prof. Thomas Blaschke Universitat Salzburg Zentrum fur Geoinformatik Hellbrunner Str. 34 5020 Salzburg Austria thomas. blaschke@ sbg. ac. at Dr. Geoffrey J. Hay University of Calgary Foothills Facility for Remote Sensing & GIScience 2500 University Dr. NW. ...

Springer eBooks, Aug 8, 2008

What is Geographic Object-Based Image Analysis (GEOBIA)? To answer this we provide a formal defin... more What is Geographic Object-Based Image Analysis (GEOBIA)? To answer this we provide a formal definition of GEOBIA, present a brief account of its coining, and propose a key objective for this new discipline. We then, conduct a SWOT 1 analysis of its potential, and discuss its main tenets and plausible future. Much still remains to be accomplished.

Lecture notes in geoinformation and cartography, 2008

The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Remote Sensing of Environment

Remote Sensing, 2011

The HEAT (Home Energy Assessment Technologies) pilot project is a FREE Geoweb mapping service, de... more The HEAT (Home Energy Assessment Technologies) pilot project is a FREE Geoweb mapping service, designed to empower the urban energy efficiency movement by allowing residents to visualize the amount and location of waste heat leaving their homes and communities as easily as clicking on their house in Google Maps. HEAT incorporates Geospatial solutions for residential waste heat monitoring using Geographic Object-Based Image Analysis (GEOBIA) and Canadian built Thermal Airborne Broadband Imager technology (TABI-320) to provide users with timely, in-depth, easy to use, location-specific waste-heat information; as well as opportunities to save their money and reduce their greenhouse gas emissions. We first report on the HEAT Phase I pilot project which evaluates 368 residences in the Brentwood community of Calgary, Alberta, Canada, and describe the development and implementation of interactive waste heat maps, energy use models, a Hot Spot tool able to view the 6+ hottest locations on each home and a new HEAT Score for inter-city waste heat comparisons. We then describe current challenges, lessons learned and new solutions as we begin Phase II and scale from 368 to 300,000+

IEEE Geoscience and Remote Sensing Letters, 2013

ABSTRACT As part of the Heat Energy Assessment Technologies (HEAT) project, we describe a novel g... more ABSTRACT As part of the Heat Energy Assessment Technologies (HEAT) project, we describe a novel geographic object-based mosaicing algorithm referred to as Object-Based Mosaicing (OBM) that joins thermal airborne flight lines around urban roof objects rather than bisecting them with arbitrary mosaic join lines. An OBM mosaic is compared with a “traditional” mosaic product (created in ENVI 4.8) consisting of 44 TABI-1800 flight lines of the City of Calgary, Alberta, Canada (825 km2). Compared with the traditional mosaic, OBM results in the following: 1) visually improved roof shapes within the scene; 2) reduced processing time (up to 50 %); 3) more accurate hot-spot detection; and 4) a better data set for more accurate home energy models-as the thermal imagery for each roof are from a single acquisition time. Conversely, without applying OBM to the full scene, 14 209 homes are bisected within the traditional mosaic product.

Lecture Notes in Geoinformation and Cartography

Forest monitoring information needs span a range of spatial, spectral and temporal scales. Forest... more Forest monitoring information needs span a range of spatial, spectral and temporal scales. Forest management and monitoring are typically enabled through the collection and interpretation of air photos, upon which spatial units are manually delineated representing areas that are homogeneous in attribution and sufficiently distinct from neighboring units. The process of acquiring, processing, and interpreting air photos is well established, understood, and relatively cost effective. As a result, the integration of other data sources or methods into this work-flow must be shown to be of value to those using forest inventory data. For example, new data sources or techniques must provide information that is currently not available from existing data and/or methods, or it must enable cost efficiencies. Traditional forest inventories may be augmented using digital 346 M.A. Wulder, J.C. White, G.J. Hay, G. Castilla remote sensing and automated approaches to provide timely information within the inventory cycle, such as disturbance or update information. In particular, image segmentation provides meaningful generalizations of image data to assist in isolating within and between stand conditions, for extrapolating sampled information over landscapes, and to reduce the impact of local radiometric and geometric variability when implementing change detection with high spatial resolution imagery. In this Chapter, we present application examples demonstrating the utility of segmentation for producing forest inventory relevant information from remotely sensed data.

Vision plays a key role as a synonym of scene-from-image reconstruction and understanding. In vis... more Vision plays a key role as a synonym of scene-from-image reconstruction and understanding. In vision, spatial information typically dominates color information (Matsuyama and Hwang, 1990). This insight was ? and still is ? the foundation of geographic object-based image analysis (GEOBIA), proposed as a viable alternative to traditional pixel-based or local window-based 1D image analysis. In computer vision (CV), spatial concepts in the scene- and image-domain, such as local shape, texture, inter-object spatial topological and spatial non-topological relationships, have been investigated since the late 1970s (Nagao and Matsuyama, 1980). In GIScience, ?object-based image analysis? (OBIA) was tentatively introduced in 2006 ( Lang and Blaschke, 2006). In 2008, it was re-formulated as GEOBIA (Hay and Castilla, 2008) emphasizing a primary focus on Earth data-derived applications and the interdisciplinary novelty of geospatio-temporal reasoning to cope with massive Earth observation (EO) i...

ISPRS International Journal of Geo-Information, 2019

The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute ... more The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analysis (OBIA) was tentatively introduced in 2006. When it was re-formulated in 2008 as geographic object-based image analysis (GEOBIA), the primary focus was on integrating multiscale EO data with GIScience and computer vision (CV) solutions to cope with the increasing spatial and temporal resolution of EO imagery. Building on recent trends in the context of big EO data analytics as well as major achievements in CV, the objective of this article is to review the role of spatial concepts in the understanding of image objects as the primary analytical units in semantic EO image analysis, and to identify opportunities where GEOBIA may support multi-source remote sensing analysis in the era of big EO data analytics. We (re-)emphasize the spatial...

Remote Sensing, 2011

Cities are complex systems composed of numerous interacting components that evolve over multiple ... more Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond "hard infrastructure" by addressing "humans as sensors", mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

Landscape Ecology, 2004

Complex systems, such as landscapes, are composed of different critical levels of organization wh... more Complex systems, such as landscapes, are composed of different critical levels of organization where interactions are stronger within levels than among levels, and where each level operates at relatively distinct time and spatial scales. To detect significant features occurring at specific levels of organization in a landscape, two steps are required. First, a multiscale dataset must be generated from which these features can emerge. Second, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce a framework for the automatic definition of multiscale landscape features using objectspecific techniques and marker-controlled watershed segmentation. By applying this framework to a high-resolution satellite scene, image-objects of varying size and shape can be delineated and studied individually at their characteristic scale of expression. This framework involves three main steps: 1͒ multiscale dataset generation using an object-specific analysis and upscaling technique, 2͒ marker-controlled watershed transformation to automatically delineate individual image-objects as they evolve through scale, and 3͒ landscape feature identification to assess the significance of these image-objects in terms of meaningful landscape features. This study was conducted on an agro-forested region in southwest Quebec, Canada, using IKONOS satellite data. Results show that image-objects tend to persist within one or two scale domains, and then suddenly disappear at the next, while new image-objects emerge at coarser scale domains. We suggest that these patterns are associated to sudden shifts in the entire image structure at certain scale domains, which may correspond to critical landscape thresholds.

ISPRS Journal of Photogrammetry and Remote Sensing, 2003

Within the conceptual framework of Complex Systems, we discuss the importance and challenges in e... more Within the conceptual framework of Complex Systems, we discuss the importance and challenges in extracting and linking multiscale objects from high-resolution remote sensing imagery to improve the monitoring, modeling and management of complex landscapes. In particular, we emphasize that remote sensing data are a particular case of the modifiable areal unit problem (MAUP) and describe how image-objects provide a way to reduce this problem. We then hypothesize that multiscale analysis should be guided by the intrinsic scale of the dominant landscape objects composing a scene and describe three different multiscale image-processing techniques with the potential to achieve this. Each of these techniques, i.e., Fractal Net Evolution Approach (FNEA), Linear Scale-Space and Blob-Feature Detection (SS), and Multiscale Object-Specific Analysis (MOSA), facilitates the multiscale pattern analysis, exploration and hierarchical linking of image-objects based on methods that derive spatially explicit multiscale contextual information from a single resolution of remote sensing imagery. We then outline the weaknesses and strengths of each technique and provide strategies for their improvement.

Photogrammetric Engineering & Remote Sensing, 2011

A potential solution to reduce high acquisition costs for airborne lidar (light detection and ran... more A potential solution to reduce high acquisition costs for airborne lidar (light detection and ranging) data is to combine lidar transects and optical satellite imagery to characterize forest vertical structure. Although multiple regression is typically used for such modeling, it seldom fully captures the complex relationships between forest variables. In an effort to improve these relationships, this study investigated the potential of Support Vector Regression (SVR), a machine learning technique, to generalize (lidar-measured) forest canopy height from four lidar transects (representing 8.8 percent, 17.6 percent, 26.4 percent and 35.2 percent area of the site) to the entire study area using QuickBird imagery. The best estimated canopy height was then linked with field measurements to predict actual canopy height, above-ground biomass (AGB) and volume. GEOgraphic Object-Based Image Analysis (GEOBIA) was used to generate all estimates at a small tree/cluster level with a mean object size (MOS) of 0.04 ha for conifer and deciduous trees. Results show that for all lidar transect samples, SVR models achieved better performance for estimating canopy height than multiple regression. By using SVR and a single lidar transect (i.e., 8.8 percent of the study area), the following relationships were found between predicted and field-measured canopy height

IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174)

Data fusion techniques are currently used to merge low resolution multispectral with high resolut... more Data fusion techniques are currently used to merge low resolution multispectral with high resolution panchromatic data. Various authors have expended efforts on attempting to achieve a fusion where the spectral characteristics of one image are combined with the spatial attributes of the second producing a new, fused image. This philosophy has a number of inherent problems. The first is that

Based on a series of empirical studies beginning in the 1970’s, it was noted that remote sensing ... more Based on a series of empirical studies beginning in the 1970’s, it was noted that remote sensing data suffered from the scale and aggregation problem. It was further recognized that there was no unique or ‘optimal’ spatial resolution for detecting the different sized, shaped, and spatially arranged entities represented in a remote sensing image of a complex scene. Today within the Earth sciences, it is strongly recognized that landscapes exhibit distinctive spatial patterns associated to different processes at different scales. Consequently, multiscale approaches are required for modern landscape analysis. It is within this context that the Multiscale Object-Specific Analysis (MOSA) framework was developed. In this paper we review the background, foundations, and recent developments of MOSA. We begin with the original definition of Object-Specific Analysis (OSA) and Object-Specific Upscaling (OSU), and continue with the recent integration of Marker Controlled Watershed Segmentation ...

As an emerging discipline, we propose a formal definition of OBIA, describe how OBIA came into ex... more As an emerging discipline, we propose a formal definition of OBIA, describe how OBIA came into existence, and as a road map to future research propose a fundamental objective. In order to provide potential strategies to meet this objective, we undertake a tentative SWOT Analysis to identify current Strengths, Weakness, Opportunities and Threats that OBIA faces, and discuss the results. * Corresponding author. ** Unless otherwise stated, this section is based on information from http://en.wikipedia.org/wiki/SWOT_Analysis *** A wiki is a type of website that allows users to add, remove, or otherwise edit and change all content very quickly and easily, sometimes without the need for registration

article i nfo It is estimated that Canada comprises approximately 28% of the world's wetlands... more article i nfo It is estimated that Canada comprises approximately 28% of the world's wetlands (~1.5 million km 2 ) providing essential ecological services such as purifying water, nutrient cycling, reducing flooding, recharging ground water supplies, and protecting shorelines. In order to better understand how wetland type and area differ over a range of spatial and thematic scales, this paper introduces a multi-scale geographic object-based image analysis (GEOBIA) approach that incorporates new object-based texture measures (geotex) and a decision-tree classifier (See5), to assess wetland differences through five common spatial resolutions (5, 10, 15, 20 and 30 m) and two different thematic classification schemes. Themes are based on (i) a Ducks Unlimited (DU: 15 class) regional classification system for wetlands in the Boreal Plain Ecosystem and (ii) the Canadian Wetland Inventory (CWI: 5 classes). Results reveal that the highest overall accuracies (67.9% and 82.2%) were achie...

This paper deals with the description and assessment of uncertainties in gridded land use data de... more This paper deals with the description and assessment of uncertainties in gridded land use data derived from Remote Sensing observations, in the context of hydrological studies. Land use is a categorical regionalised variable returning the main socioeconomic role each location has, where the role is inferred from the pattern of occupation of land. There are two main uncertainties surrounding land use data, positional and categorical. This paper focuses on the second one, as the first one has in general less serious implications and is easier to tackle. The conventional method used to asess categorical uncertainty, the confusion matrix, is criticised in depth, the main critique being its inability to inform on a basic requirement to propagate uncertainty through distributed hydrological models, namely the spatial distribution of errors. Some existing alternative methods are reported, and finally the need for metadata is stressed as a more reliable means to assess the quality, and hence the uncertainty, of these data.

Springer eBooks, Aug 27, 2008

Editors Prof. Thomas Blaschke Universitat Salzburg Zentrum fur Geoinformatik Hellbrunner Str. 34 ... more Editors Prof. Thomas Blaschke Universitat Salzburg Zentrum fur Geoinformatik Hellbrunner Str. 34 5020 Salzburg Austria thomas. blaschke@ sbg. ac. at Dr. Geoffrey J. Hay University of Calgary Foothills Facility for Remote Sensing & GIScience 2500 University Dr. NW. ...

Springer eBooks, Aug 8, 2008

What is Geographic Object-Based Image Analysis (GEOBIA)? To answer this we provide a formal defin... more What is Geographic Object-Based Image Analysis (GEOBIA)? To answer this we provide a formal definition of GEOBIA, present a brief account of its coining, and propose a key objective for this new discipline. We then, conduct a SWOT 1 analysis of its potential, and discuss its main tenets and plausible future. Much still remains to be accomplished.

Lecture notes in geoinformation and cartography, 2008

The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Remote Sensing of Environment

Remote Sensing, 2011

The HEAT (Home Energy Assessment Technologies) pilot project is a FREE Geoweb mapping service, de... more The HEAT (Home Energy Assessment Technologies) pilot project is a FREE Geoweb mapping service, designed to empower the urban energy efficiency movement by allowing residents to visualize the amount and location of waste heat leaving their homes and communities as easily as clicking on their house in Google Maps. HEAT incorporates Geospatial solutions for residential waste heat monitoring using Geographic Object-Based Image Analysis (GEOBIA) and Canadian built Thermal Airborne Broadband Imager technology (TABI-320) to provide users with timely, in-depth, easy to use, location-specific waste-heat information; as well as opportunities to save their money and reduce their greenhouse gas emissions. We first report on the HEAT Phase I pilot project which evaluates 368 residences in the Brentwood community of Calgary, Alberta, Canada, and describe the development and implementation of interactive waste heat maps, energy use models, a Hot Spot tool able to view the 6+ hottest locations on each home and a new HEAT Score for inter-city waste heat comparisons. We then describe current challenges, lessons learned and new solutions as we begin Phase II and scale from 368 to 300,000+

IEEE Geoscience and Remote Sensing Letters, 2013

ABSTRACT As part of the Heat Energy Assessment Technologies (HEAT) project, we describe a novel g... more ABSTRACT As part of the Heat Energy Assessment Technologies (HEAT) project, we describe a novel geographic object-based mosaicing algorithm referred to as Object-Based Mosaicing (OBM) that joins thermal airborne flight lines around urban roof objects rather than bisecting them with arbitrary mosaic join lines. An OBM mosaic is compared with a “traditional” mosaic product (created in ENVI 4.8) consisting of 44 TABI-1800 flight lines of the City of Calgary, Alberta, Canada (825 km2). Compared with the traditional mosaic, OBM results in the following: 1) visually improved roof shapes within the scene; 2) reduced processing time (up to 50 %); 3) more accurate hot-spot detection; and 4) a better data set for more accurate home energy models-as the thermal imagery for each roof are from a single acquisition time. Conversely, without applying OBM to the full scene, 14 209 homes are bisected within the traditional mosaic product.

Lecture Notes in Geoinformation and Cartography

Forest monitoring information needs span a range of spatial, spectral and temporal scales. Forest... more Forest monitoring information needs span a range of spatial, spectral and temporal scales. Forest management and monitoring are typically enabled through the collection and interpretation of air photos, upon which spatial units are manually delineated representing areas that are homogeneous in attribution and sufficiently distinct from neighboring units. The process of acquiring, processing, and interpreting air photos is well established, understood, and relatively cost effective. As a result, the integration of other data sources or methods into this work-flow must be shown to be of value to those using forest inventory data. For example, new data sources or techniques must provide information that is currently not available from existing data and/or methods, or it must enable cost efficiencies. Traditional forest inventories may be augmented using digital 346 M.A. Wulder, J.C. White, G.J. Hay, G. Castilla remote sensing and automated approaches to provide timely information within the inventory cycle, such as disturbance or update information. In particular, image segmentation provides meaningful generalizations of image data to assist in isolating within and between stand conditions, for extrapolating sampled information over landscapes, and to reduce the impact of local radiometric and geometric variability when implementing change detection with high spatial resolution imagery. In this Chapter, we present application examples demonstrating the utility of segmentation for producing forest inventory relevant information from remotely sensed data.

Vision plays a key role as a synonym of scene-from-image reconstruction and understanding. In vis... more Vision plays a key role as a synonym of scene-from-image reconstruction and understanding. In vision, spatial information typically dominates color information (Matsuyama and Hwang, 1990). This insight was ? and still is ? the foundation of geographic object-based image analysis (GEOBIA), proposed as a viable alternative to traditional pixel-based or local window-based 1D image analysis. In computer vision (CV), spatial concepts in the scene- and image-domain, such as local shape, texture, inter-object spatial topological and spatial non-topological relationships, have been investigated since the late 1970s (Nagao and Matsuyama, 1980). In GIScience, ?object-based image analysis? (OBIA) was tentatively introduced in 2006 ( Lang and Blaschke, 2006). In 2008, it was re-formulated as GEOBIA (Hay and Castilla, 2008) emphasizing a primary focus on Earth data-derived applications and the interdisciplinary novelty of geospatio-temporal reasoning to cope with massive Earth observation (EO) i...

ISPRS International Journal of Geo-Information, 2019

The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute ... more The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analysis (OBIA) was tentatively introduced in 2006. When it was re-formulated in 2008 as geographic object-based image analysis (GEOBIA), the primary focus was on integrating multiscale EO data with GIScience and computer vision (CV) solutions to cope with the increasing spatial and temporal resolution of EO imagery. Building on recent trends in the context of big EO data analytics as well as major achievements in CV, the objective of this article is to review the role of spatial concepts in the understanding of image objects as the primary analytical units in semantic EO image analysis, and to identify opportunities where GEOBIA may support multi-source remote sensing analysis in the era of big EO data analytics. We (re-)emphasize the spatial...

Remote Sensing, 2011

Cities are complex systems composed of numerous interacting components that evolve over multiple ... more Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond "hard infrastructure" by addressing "humans as sensors", mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

Landscape Ecology, 2004

Complex systems, such as landscapes, are composed of different critical levels of organization wh... more Complex systems, such as landscapes, are composed of different critical levels of organization where interactions are stronger within levels than among levels, and where each level operates at relatively distinct time and spatial scales. To detect significant features occurring at specific levels of organization in a landscape, two steps are required. First, a multiscale dataset must be generated from which these features can emerge. Second, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce a framework for the automatic definition of multiscale landscape features using objectspecific techniques and marker-controlled watershed segmentation. By applying this framework to a high-resolution satellite scene, image-objects of varying size and shape can be delineated and studied individually at their characteristic scale of expression. This framework involves three main steps: 1͒ multiscale dataset generation using an object-specific analysis and upscaling technique, 2͒ marker-controlled watershed transformation to automatically delineate individual image-objects as they evolve through scale, and 3͒ landscape feature identification to assess the significance of these image-objects in terms of meaningful landscape features. This study was conducted on an agro-forested region in southwest Quebec, Canada, using IKONOS satellite data. Results show that image-objects tend to persist within one or two scale domains, and then suddenly disappear at the next, while new image-objects emerge at coarser scale domains. We suggest that these patterns are associated to sudden shifts in the entire image structure at certain scale domains, which may correspond to critical landscape thresholds.

ISPRS Journal of Photogrammetry and Remote Sensing, 2003

Within the conceptual framework of Complex Systems, we discuss the importance and challenges in e... more Within the conceptual framework of Complex Systems, we discuss the importance and challenges in extracting and linking multiscale objects from high-resolution remote sensing imagery to improve the monitoring, modeling and management of complex landscapes. In particular, we emphasize that remote sensing data are a particular case of the modifiable areal unit problem (MAUP) and describe how image-objects provide a way to reduce this problem. We then hypothesize that multiscale analysis should be guided by the intrinsic scale of the dominant landscape objects composing a scene and describe three different multiscale image-processing techniques with the potential to achieve this. Each of these techniques, i.e., Fractal Net Evolution Approach (FNEA), Linear Scale-Space and Blob-Feature Detection (SS), and Multiscale Object-Specific Analysis (MOSA), facilitates the multiscale pattern analysis, exploration and hierarchical linking of image-objects based on methods that derive spatially explicit multiscale contextual information from a single resolution of remote sensing imagery. We then outline the weaknesses and strengths of each technique and provide strategies for their improvement.

Photogrammetric Engineering & Remote Sensing, 2011

A potential solution to reduce high acquisition costs for airborne lidar (light detection and ran... more A potential solution to reduce high acquisition costs for airborne lidar (light detection and ranging) data is to combine lidar transects and optical satellite imagery to characterize forest vertical structure. Although multiple regression is typically used for such modeling, it seldom fully captures the complex relationships between forest variables. In an effort to improve these relationships, this study investigated the potential of Support Vector Regression (SVR), a machine learning technique, to generalize (lidar-measured) forest canopy height from four lidar transects (representing 8.8 percent, 17.6 percent, 26.4 percent and 35.2 percent area of the site) to the entire study area using QuickBird imagery. The best estimated canopy height was then linked with field measurements to predict actual canopy height, above-ground biomass (AGB) and volume. GEOgraphic Object-Based Image Analysis (GEOBIA) was used to generate all estimates at a small tree/cluster level with a mean object size (MOS) of 0.04 ha for conifer and deciduous trees. Results show that for all lidar transect samples, SVR models achieved better performance for estimating canopy height than multiple regression. By using SVR and a single lidar transect (i.e., 8.8 percent of the study area), the following relationships were found between predicted and field-measured canopy height

IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174)

Data fusion techniques are currently used to merge low resolution multispectral with high resolut... more Data fusion techniques are currently used to merge low resolution multispectral with high resolution panchromatic data. Various authors have expended efforts on attempting to achieve a fusion where the spectral characteristics of one image are combined with the spatial attributes of the second producing a new, fused image. This philosophy has a number of inherent problems. The first is that

Based on a series of empirical studies beginning in the 1970’s, it was noted that remote sensing ... more Based on a series of empirical studies beginning in the 1970’s, it was noted that remote sensing data suffered from the scale and aggregation problem. It was further recognized that there was no unique or ‘optimal’ spatial resolution for detecting the different sized, shaped, and spatially arranged entities represented in a remote sensing image of a complex scene. Today within the Earth sciences, it is strongly recognized that landscapes exhibit distinctive spatial patterns associated to different processes at different scales. Consequently, multiscale approaches are required for modern landscape analysis. It is within this context that the Multiscale Object-Specific Analysis (MOSA) framework was developed. In this paper we review the background, foundations, and recent developments of MOSA. We begin with the original definition of Object-Specific Analysis (OSA) and Object-Specific Upscaling (OSU), and continue with the recent integration of Marker Controlled Watershed Segmentation ...

As an emerging discipline, we propose a formal definition of OBIA, describe how OBIA came into ex... more As an emerging discipline, we propose a formal definition of OBIA, describe how OBIA came into existence, and as a road map to future research propose a fundamental objective. In order to provide potential strategies to meet this objective, we undertake a tentative SWOT Analysis to identify current Strengths, Weakness, Opportunities and Threats that OBIA faces, and discuss the results. * Corresponding author. ** Unless otherwise stated, this section is based on information from http://en.wikipedia.org/wiki/SWOT_Analysis *** A wiki is a type of website that allows users to add, remove, or otherwise edit and change all content very quickly and easily, sometimes without the need for registration

article i nfo It is estimated that Canada comprises approximately 28% of the world's wetlands... more article i nfo It is estimated that Canada comprises approximately 28% of the world's wetlands (~1.5 million km 2 ) providing essential ecological services such as purifying water, nutrient cycling, reducing flooding, recharging ground water supplies, and protecting shorelines. In order to better understand how wetland type and area differ over a range of spatial and thematic scales, this paper introduces a multi-scale geographic object-based image analysis (GEOBIA) approach that incorporates new object-based texture measures (geotex) and a decision-tree classifier (See5), to assess wetland differences through five common spatial resolutions (5, 10, 15, 20 and 30 m) and two different thematic classification schemes. Themes are based on (i) a Ducks Unlimited (DU: 15 class) regional classification system for wetlands in the Boreal Plain Ecosystem and (ii) the Canadian Wetland Inventory (CWI: 5 classes). Results reveal that the highest overall accuracies (67.9% and 82.2%) were achie...