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Geoffrey J. Hay
Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada

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Review
Published: 23 June 2020 in Remote Sensing
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Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial); (ii) best practices informed by research; and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.

ACS Style

Maja Kucharczyk; Geoffrey Hay; Salar Ghaffarian; Chris Hugenholtz. Geographic Object-Based Image Analysis: A Primer and Future Directions. Remote Sensing 2020, 12, 2012 .

AMA Style

Maja Kucharczyk, Geoffrey Hay, Salar Ghaffarian, Chris Hugenholtz. Geographic Object-Based Image Analysis: A Primer and Future Directions. Remote Sensing. 2020; 12 (12):2012.

Chicago/Turabian Style

Maja Kucharczyk; Geoffrey Hay; Salar Ghaffarian; Chris Hugenholtz. 2020. "Geographic Object-Based Image Analysis: A Primer and Future Directions." Remote Sensing 12, no. 12: 2012.

Review
Published: 24 October 2019 in ISPRS International Journal of Geo-Information
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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 paradigm as a key requisite for an image understanding system capable to deal with and exploit the massive data streams we are currently facing; a system which encompasses a combined physical and statistical model-based inference engine, a well-structured CV system design based on a convergence of spatial and colour evidence, semantic content-based image retrieval capacities, and the full integration of spatio-temporal aspects of the studied geographical phenomena.

ACS Style

Stefan Lang; Geoffrey J. Hay; Andrea Baraldi; Dirk Tiede; Thomas Blaschke. Geobia Achievements and Spatial Opportunities in the Era of Big Earth Observation Data. ISPRS International Journal of Geo-Information 2019, 8, 474 .

AMA Style

Stefan Lang, Geoffrey J. Hay, Andrea Baraldi, Dirk Tiede, Thomas Blaschke. Geobia Achievements and Spatial Opportunities in the Era of Big Earth Observation Data. ISPRS International Journal of Geo-Information. 2019; 8 (11):474.

Chicago/Turabian Style

Stefan Lang; Geoffrey J. Hay; Andrea Baraldi; Dirk Tiede; Thomas Blaschke. 2019. "Geobia Achievements and Spatial Opportunities in the Era of Big Earth Observation Data." ISPRS International Journal of Geo-Information 8, no. 11: 474.

Journal article
Published: 29 November 2018 in ISPRS International Journal of Geo-Information
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The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing data only. To achieve this, we (1) conduct a review of the urban remote sensing vegetation classification literature, and we then (2) discuss methods to derive a detailed map of VOR for a study area in Calgary, Alberta, Canada from a late season, high-resolution airborne orthomosaic based on an integration of Geographic Object-Based Image Analysis (GEOBIA), pre-classification filtering of image-objects using Volunteered Geographic Information (VGI), and a machine learning classifier. Pre-classification filtering lowered the computational burden of classification by reducing the number of input objects by 14%. Accuracy assessment results show that, despite the presence of senescing vegetation with low vegetation index values and deep shadows, classification using a small number of image-object spectral attributes as classification features (n = 9) had similar overall accuracy (88.5%) to a much more complex classification (91.8%) comprising a comprehensive set of spectral, texture, and spatial attributes as classification features (n = 86). This research provides an example of the very specific questions answerable about precise urban locations using a combination of high-resolution passive imagery and freely available VGI data. It highlights the benefits of pre-classification filtering and the judicious selection of features from image-object attributes to reduce processing load without sacrificing classification accuracy.

ACS Style

David C. Griffith; Geoffrey J. Hay. Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops. ISPRS International Journal of Geo-Information 2018, 7, 462 .

AMA Style

David C. Griffith, Geoffrey J. Hay. Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops. ISPRS International Journal of Geo-Information. 2018; 7 (12):462.

Chicago/Turabian Style

David C. Griffith; Geoffrey J. Hay. 2018. "Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops." ISPRS International Journal of Geo-Information 7, no. 12: 462.

Journal article
Published: 27 November 2014 in Remote Sensing
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Thermal Infrared (TIR) remote sensing images of urban environments are increasingly available from airborne and satellite platforms. However, limited access to high-spatial resolution (H-res: ~1 m) TIR satellite images requires the use of TIR airborne sensors for mapping large complex urban surfaces, especially at micro-scales. A critical limitation of such H-res mapping is the need to acquire a large scene composed of multiple flight lines and mosaic them together. This results in the same scene components (e.g., roads, buildings, green space and water) exhibiting different temperatures in different flight lines. To mitigate these effects, linear relative radiometric normalization (RRN) techniques are often applied. However, the Earth’s surface is composed of features whose thermal behaviour is characterized by complexity and non-linearity. Therefore, we hypothesize that non-linear RRN techniques should demonstrate increased radiometric agreement over similar linear techniques. To test this hypothesis, this paper evaluates four (linear and non-linear) RRN techniques, including: (i) histogram matching (HM); (ii) pseudo-invariant feature-based polynomial regression (PIF_Poly); (iii) no-change stratified random sample-based linear regression (NCSRS_Lin); and (iv) no-change stratified random sample-based polynomial regression (NCSRS_Poly); two of which (ii and iv) are newly proposed non-linear techniques. When applied over two adjacent flight lines (~70 km2) of TABI-1800 airborne data, visual and statistical results show that both new non-linear techniques improved radiometric agreement over the previously evaluated linear techniques, with the new fully-automated method, NCSRS-based polynomial regression, providing the highest improvement in radiometric agreement between the master and the slave images, at ~56%. This is ~5% higher than the best previously evaluated linear technique (NCSRS-based linear regression).

ACS Style

Mir Mustafizur Rahman; Geoffrey J. Hay; Isabelle Couloigner; Bharanidharan Hemachandran; Jeremy Bailin. An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery. Remote Sensing 2014, 6, 11810 -11828.

AMA Style

Mir Mustafizur Rahman, Geoffrey J. Hay, Isabelle Couloigner, Bharanidharan Hemachandran, Jeremy Bailin. An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery. Remote Sensing. 2014; 6 (12):11810-11828.

Chicago/Turabian Style

Mir Mustafizur Rahman; Geoffrey J. Hay; Isabelle Couloigner; Bharanidharan Hemachandran; Jeremy Bailin. 2014. "An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery." Remote Sensing 6, no. 12: 11810-11828.

Journal article
Published: 13 October 2014 in Remote Sensing
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The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, Geographic Object-Based Image Analysis (GEOBIA), and a Geoweb environment to allow the residents of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and the city. To ensure the accuracy of these measures, the correct emissivity of roof materials needs to be known. However, roof material information is not readily available in the Canadian public domain. To overcome this challenge, a unique Volunteered Geographic Information (VGI) application was developed using Google Street View that engages citizens to classify the roof materials of single dwelling residences in a simple and intuitive manner. Since data credibility, quality, and accuracy are major concerns when using VGI, a private Multiple Listing Services (MLS) dataset was used for cross-verification. From May–November 2013, 1244 volunteers from 85 cities and 14 countries classified 1815 roofs in the study area. Results show (I) a 72% match between the VGI and MLS data; and (II) in the majority of cases, roofs with greater than, or equal to five contributions have the same material defined in both datasets. Additionally, this research meets new challenges to the GEOBIA community to incorporate existing GIS vector data within an object-based workflow and engages the public to provide volunteered information for urban objects from which new geo-intelligence is created in support of urban energy efficiency.

ACS Style

Bilal Abdulkarim; Rustam Kamberov; Geoffrey J. Hay. Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API. Remote Sensing 2014, 6, 9691 -9711.

AMA Style

Bilal Abdulkarim, Rustam Kamberov, Geoffrey J. Hay. Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API. Remote Sensing. 2014; 6 (10):9691-9711.

Chicago/Turabian Style

Bilal Abdulkarim; Rustam Kamberov; Geoffrey J. Hay. 2014. "Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API." Remote Sensing 6, no. 10: 9691-9711.

Journal article
Published: 01 October 2014 in Remote Sensing
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In an effort to minimize complex urban microclimatic variability within high-resolution (H-Res) airborne thermal infrared (TIR) flight-lines, we describe the Thermal Urban Road Normalization (TURN) algorithm, which is based on the idea of pseudo invariant features. By assuming a homogeneous road temperature within a TIR scene, we hypothesize that any variation observed in road temperature is the effect of local microclimatic variability. To model microclimatic variability, we define a road-object class (Road), compute the within-Road temperature variability, sample it at different spatial intervals (i.e., 10, 20, 50, and 100 m) then interpolate samples over each flight-line to create an object-weighted variable temperature field (a TURN-surface). The optimal TURN-surface is then subtracted from the original TIR image, essentially creating a microclimate-free scene. Results at different sampling intervals are assessed based on their: (i) ability to visually and statistically reduce overall scene variability and (ii) computation speed. TURN is evaluated on three non-adjacent TABI-1800 flight-lines (~182 km2) that were acquired in 2012 at night over The City of Calgary, Alberta, Canada. TURN also meets a recent GEOBIA (Geospatial Object Based Image Analysis) challenge by incorporating existing GIS vector objects within the GEOBIA workflow, rather than relying exclusively on segmentation methods.

ACS Style

Mir Mustafizur Rahman; Geoffrey J. Hay; Isabelle Couloigner; Bharanidharan Hemachandran. Transforming Image-Objects into Multiscale Fields: A GEOBIA Approach to Mitigate Urban Microclimatic Variability within H-Res Thermal Infrared Airborne Flight-Lines. Remote Sensing 2014, 6, 9435 -9457.

AMA Style

Mir Mustafizur Rahman, Geoffrey J. Hay, Isabelle Couloigner, Bharanidharan Hemachandran. Transforming Image-Objects into Multiscale Fields: A GEOBIA Approach to Mitigate Urban Microclimatic Variability within H-Res Thermal Infrared Airborne Flight-Lines. Remote Sensing. 2014; 6 (10):9435-9457.

Chicago/Turabian Style

Mir Mustafizur Rahman; Geoffrey J. Hay; Isabelle Couloigner; Bharanidharan Hemachandran. 2014. "Transforming Image-Objects into Multiscale Fields: A GEOBIA Approach to Mitigate Urban Microclimatic Variability within H-Res Thermal Infrared Airborne Flight-Lines." Remote Sensing 6, no. 10: 9435-9457.

Journal article
Published: 30 April 2012 in International Journal of Applied Earth Observation and Geoinformation
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ACS Style

Gang Chen; Geoffrey J. Hay; Benoît St-Onge. A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada. International Journal of Applied Earth Observation and Geoinformation 2012, 15, 28 -37.

AMA Style

Gang Chen, Geoffrey J. Hay, Benoît St-Onge. A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada. International Journal of Applied Earth Observation and Geoinformation. 2012; 15 ():28-37.

Chicago/Turabian Style

Gang Chen; Geoffrey J. Hay; Benoît St-Onge. 2012. "A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada." International Journal of Applied Earth Observation and Geoinformation 15, no. : 28-37.

Journal article
Published: 15 February 2012 in Remote Sensing of Environment
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It is estimated that Canada comprises approximately 28% of the world's wetlands (~ 1.5 million km2) 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 achieved at the 10 m spatial resolution for both the DU and CWI classification schemes respectively. It was also found that the DU wetland types experienced greater area differences through scale with the largest differences for both classification schemes occurring in classes with a large treed component. Results further show that the inclusion of geotex channels (generated from dynamically sized and shaped window that measures the spatial variability of the wetland components composing a scene, rather than of individual pixels within a fixed sized window) improved wetland classification.

ACS Style

Ryan P. Powers; Geoffrey J. Hay; Gang Chen. How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains. Remote Sensing of Environment 2012, 117, 135 -145.

AMA Style

Ryan P. Powers, Geoffrey J. Hay, Gang Chen. How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains. Remote Sensing of Environment. 2012; 117 ():135-145.

Chicago/Turabian Style

Ryan P. Powers; Geoffrey J. Hay; Gang Chen. 2012. "How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains." Remote Sensing of Environment 117, no. : 135-145.

Original articles
Published: 30 September 2011 in International Journal of Remote Sensing
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Geographically weighted regression (GWR) extends the conventional ordinary least squares (OLS) regression technique by considering spatial nonstationarity in variable relationships and allowing the use of spatially varying coefficients in linear models. Previous forest studies have demonstrated the better performance of GWR compared to OLS when calibrated and validated at sampled locations where field measurements are collected. However, the use of GWR for remote-sensing applications requires generating estimates and evaluating the model performance for the large image scene, not just for sampled locations. In this study, we introduce GWR to estimate forest canopy height using high spatial resolution Quickbird (QB) imagery and evaluate the influence of sampling density on GWR. We also examine four commonly used spatial analysis techniques – OLS, inverse distance weighting (IDW), ordinary kriging (OK) and cokriging (COK) – and compare their performance with that using GWR. Results show that (i) GWR outperformed OLS at all sampling densities; however, they produced similar results at low sampling densities, suggesting that GWR may not produce significantly better results than OLS in remote-sensing operational applications where only a small number of field data are collected. (ii) The performance of GWR was better than those of IDW, OK and COK at most sampling densities. Among the spatial interpolation techniques we examined, IDW was the best to estimate the canopy height at most densities, while COK outperformed OK only marginally and produced larger canopy height estimation errors than both IDW and GWR. (iii) GWR had the advantage of generating canopy height estimation maps with more accurate estimates than OLS, and it preserved patterns of geographic features better than IDW, OK or COK.

ACS Style

Gang Chen; Kaiguang Zhao; Gregory J. McDermid; Geoffrey J. Hay. The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data. International Journal of Remote Sensing 2011, 33, 2909 -2924.

AMA Style

Gang Chen, Kaiguang Zhao, Gregory J. McDermid, Geoffrey J. Hay. The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data. International Journal of Remote Sensing. 2011; 33 (9):2909-2924.

Chicago/Turabian Style

Gang Chen; Kaiguang Zhao; Gregory J. McDermid; Geoffrey J. Hay. 2011. "The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data." International Journal of Remote Sensing 33, no. 9: 2909-2924.

Review
Published: 19 August 2011 in Remote Sensing
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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.

ACS Style

Thomas Blaschke; Geoffrey J. Hay; Qihao Weng; Bernd Resch. Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview. Remote Sensing 2011, 3, 1743 -1776.

AMA Style

Thomas Blaschke, Geoffrey J. Hay, Qihao Weng, Bernd Resch. Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview. Remote Sensing. 2011; 3 (8):1743-1776.

Chicago/Turabian Style

Thomas Blaschke; Geoffrey J. Hay; Qihao Weng; Bernd Resch. 2011. "Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview." Remote Sensing 3, no. 8: 1743-1776.

Journal article
Published: 05 July 2011 in Remote Sensing
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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 green-house-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+ homes with the newly developed TABI-1800. Specifically, we introduce a new object-based mosaicing strategy, an adaptation of Emissivity Modulation to correct for emissivity differences, a new Thermal Urban Road Normalization (TURN) technique to correct for scene-wide microclimatic variation. We also describe a new Carbon Score and opportunities to update city cadastral errors with automatically defined thermal house objects.

ACS Style

Geoffrey J. Hay; Christopher Kyle; Bharanidharan Hemachandran; Gang Chen; Mir Mustafizur Rahman; Tak S. Fung; Joseph L. Arvai. Geospatial Technologies to Improve Urban Energy Efficiency. Remote Sensing 2011, 3, 1380 -1405.

AMA Style

Geoffrey J. Hay, Christopher Kyle, Bharanidharan Hemachandran, Gang Chen, Mir Mustafizur Rahman, Tak S. Fung, Joseph L. Arvai. Geospatial Technologies to Improve Urban Energy Efficiency. Remote Sensing. 2011; 3 (7):1380-1405.

Chicago/Turabian Style

Geoffrey J. Hay; Christopher Kyle; Bharanidharan Hemachandran; Gang Chen; Mir Mustafizur Rahman; Tak S. Fung; Joseph L. Arvai. 2011. "Geospatial Technologies to Improve Urban Energy Efficiency." Remote Sensing 3, no. 7: 1380-1405.

Original articles
Published: 01 June 2011 in International Journal of Geographical Information Science
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Lidar (light detection and ranging) has demonstrated the ability to provide highly accurate information on forest vertical structure; however, lidar data collection and processing are still expensive. Very high spatial resolution optical remotely sensed data have also shown promising results to delineate various forest biophysical properties. In this study, our main objective is to examine the potential of Quickbird (QB) imagery to accurately estimate forest canopy heights measured from small-footprint lidar data. To achieve this, we have developed multiscale geographic object-based image analysis (GEOBIA) models from QB data for both deciduous and conifer stands. In addition to the spectral information, these models also included (1) image-texture [i.e., an internal-object variability measure and a new dynamic geographic object-based texture (GEOTEX) measure that quantifies forest variability within neighboring objects] and (2) a canopy shadow fraction measure that acts as a proxy of vertical forest structure. A novel object area-weighted error calculation approach was used to evaluate model performance by considering the importance of object size. To determine the best object scale [i.e., mean object size (MOS)] for defining the most accurate canopy height estimates, we introduce a new perspective, which considers height variability both between- and within-objects at all scales. To better evaluate the improvements resulting from our GEOBIA models, we compared their performance with a traditional pixel-based approach. Our results show that (1) the addition of image-texture and shadow fraction variables increases the model performance versus using spectral information only, especially for deciduous trees, where the average increase of R 2 is approximately 23% with a further 1.47 m decrease of Root Mean Squared Error (RMSE) at all scales using the GEOBIA approach; (2) the best object scale for our study site corresponds to an MOS of 4.00 ha; (3) at most scales, GEOBIA models achieve more accurate results than pixel-based models; however, we note that inappropriately selected object scales may result in poorer height accuracies than those derived from the applied pixel-based approach.

ACS Style

Gang Chen; Geoffrey J. Hay; Guillermo Castilla; Benoît St-Onge; Ryan Powers. A multiscale geographic object-based image analysis to estimate lidar-measured forest canopy height using Quickbird imagery. International Journal of Geographical Information Science 2011, 25, 877 -893.

AMA Style

Gang Chen, Geoffrey J. Hay, Guillermo Castilla, Benoît St-Onge, Ryan Powers. A multiscale geographic object-based image analysis to estimate lidar-measured forest canopy height using Quickbird imagery. International Journal of Geographical Information Science. 2011; 25 (6):877-893.

Chicago/Turabian Style

Gang Chen; Geoffrey J. Hay; Guillermo Castilla; Benoît St-Onge; Ryan Powers. 2011. "A multiscale geographic object-based image analysis to estimate lidar-measured forest canopy height using Quickbird imagery." International Journal of Geographical Information Science 25, no. 6: 877-893.

Journal article
Published: 01 August 2009 in Photogrammetric Engineering & Remote Sensing
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ACS Style

Guillermo Castilla; Richard H. Guthrie; Geoffrey J. Hay. The Land-cover Change Mapper (LCM) and its Application to Timber Harvest Monitoring in Western Canada. Photogrammetric Engineering & Remote Sensing 2009, 75, 941 -950.

AMA Style

Guillermo Castilla, Richard H. Guthrie, Geoffrey J. Hay. The Land-cover Change Mapper (LCM) and its Application to Timber Harvest Monitoring in Western Canada. Photogrammetric Engineering & Remote Sensing. 2009; 75 (8):941-950.

Chicago/Turabian Style

Guillermo Castilla; Richard H. Guthrie; Geoffrey J. Hay. 2009. "The Land-cover Change Mapper (LCM) and its Application to Timber Harvest Monitoring in Western Canada." Photogrammetric Engineering & Remote Sensing 75, no. 8: 941-950.

Journal article
Published: 20 March 2009 in Computers & Geosciences
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LiDAR canopy height models (CHMs) can exhibit unnatural looking holes or pits, i.e., pixels with a much lower digital number than their immediate neighbors. These artifacts may be caused by a combination of factors, from data acquisition to post-processing, that not only result in a noisy appearance to the CHM but may also limit semi-automated tree-crown delineation and lead to errors in biomass estimates. We present a highly effective semi-automated pit filling algorithm that interactively detects data pits based on a simple user-defined threshold, and then fills them with a value derived from their neighborhood. We briefly describe this algorithm and its graphical user interface, and show its result in a LiDAR CHM populated with data pits. This method can be rapidly applied to any CHM with minimal user interaction. Visualization confirms that our method effectively and quickly removes data pits.

ACS Style

Joshua R. Ben-Arie; Geoffrey J. Hay; Ryan P. Powers; Guillermo Castilla; Benoît St-Onge. Development of a pit filling algorithm for LiDAR canopy height models. Computers & Geosciences 2009, 35, 1940 -1949.

AMA Style

Joshua R. Ben-Arie, Geoffrey J. Hay, Ryan P. Powers, Guillermo Castilla, Benoît St-Onge. Development of a pit filling algorithm for LiDAR canopy height models. Computers & Geosciences. 2009; 35 (9):1940-1949.

Chicago/Turabian Style

Joshua R. Ben-Arie; Geoffrey J. Hay; Ryan P. Powers; Guillermo Castilla; Benoît St-Onge. 2009. "Development of a pit filling algorithm for LiDAR canopy height models." Computers & Geosciences 35, no. 9: 1940-1949.

Book chapter
Published: 08 August 2008 in Lecture Notes in Geoinformation and Cartography
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Object-Based Image Analysis (OBIA) has gained considerable impetus over the last decade. However, despite the many newly developed methods and the numerous successful case studies, little effort has been directed towards building the conceptual foundations underlying it. In particular, there are at least two questions that need a clear answer before OBIA can be considered a discipline: i) What is the definition and ontological status of both image objects and geographic objects? And ii) How do they relate to each other? This chapter provides the authors’ tentative response to these questions.

ACS Style

G. Castilla; G. J. Hay. Image objects and geographic objects. Lecture Notes in Geoinformation and Cartography 2008, 91 -110.

AMA Style

G. Castilla, G. J. Hay. Image objects and geographic objects. Lecture Notes in Geoinformation and Cartography. 2008; ():91-110.

Chicago/Turabian Style

G. Castilla; G. J. Hay. 2008. "Image objects and geographic objects." Lecture Notes in Geoinformation and Cartography , no. : 91-110.

Book chapter
Published: 08 August 2008 in Lecture Notes in Geoinformation and Cartography
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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 SWOT1 analysis of its potential, and discuss its main tenets and plausible future. Much still remains to be accomplished.

ACS Style

G. J. Hay; Guillermo Castilla. Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. Lecture Notes in Geoinformation and Cartography 2008, 75 -89.

AMA Style

G. J. Hay, Guillermo Castilla. Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. Lecture Notes in Geoinformation and Cartography. 2008; ():75-89.

Chicago/Turabian Style

G. J. Hay; Guillermo Castilla. 2008. "Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline." Lecture Notes in Geoinformation and Cartography , no. : 75-89.

Book chapter
Published: 08 August 2008 in Lecture Notes in Geoinformation and Cartography
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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 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.

ACS Style

M.A. Wulder; J.C. White; G.J. Hay; Guillermo Castilla. Pixels to objects to information: Spatial context to aid in forest characterization with remote sensing. Lecture Notes in Geoinformation and Cartography 2008, 345 -363.

AMA Style

M.A. Wulder, J.C. White, G.J. Hay, Guillermo Castilla. Pixels to objects to information: Spatial context to aid in forest characterization with remote sensing. Lecture Notes in Geoinformation and Cartography. 2008; ():345-363.

Chicago/Turabian Style

M.A. Wulder; J.C. White; G.J. Hay; Guillermo Castilla. 2008. "Pixels to objects to information: Spatial context to aid in forest characterization with remote sensing." Lecture Notes in Geoinformation and Cartography , no. : 345-363.