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. ...
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.
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...
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...
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. ...
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.
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...
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...
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