scholarly journals A Framework for Geographic Object-Based Image Analysis (GEOBIA) based on geographic ontology

2015 ◽  
Vol XL-7/W4 ◽  
pp. 27-33 ◽  
Author(s):  
H. Y. Gu ◽  
H. T. Li ◽  
L. Yan ◽  
X. J. Lu
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Systematic Approach ◽  
Semantic Network ◽  
Image Objects ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Objective Model

GEOBIA (Geographic Object-Based Image Analysis) is not only a hot topic of current remote sensing and geographical research. It is believed to be a paradigm in remote sensing and GIScience. The lack of a systematic approach designed to conceptualize and formalize the class definitions makes GEOBIA a highly subjective and difficult method to reproduce. This paper aims to put forward a framework for GEOBIA based on geographic ontology theory, which could implement "Geographic entities - Image objects - Geographic objects" true reappearance. It consists of three steps, first, geographical entities are described by geographic ontology, second, semantic network model is built based on OWL(ontology web language), at last, geographical objects are classified with decision rule or other classifiers. A case study of farmland ontology was conducted for describing the framework. The strength of this framework is that it provides interpretation strategies and global framework for GEOBIA with the property of objective, overall, universal, universality, etc., which avoids inconsistencies caused by different experts’ experience and provides an objective model for mage analysis.

scholarly journals Geographic Object-Based Image Analysis: A Primer and Future Directions

2020 ◽  
Vol 12 (12) ◽  
pp. 2012 ◽  
Author(s):  
Maja Kucharczyk ◽  
Geoffrey J. Hay ◽  
Salar Ghaffarian ◽  
Chris H. Hugenholtz
Keyword(s):  
Image Analysis ◽  
Current Status ◽  
Future Research ◽  
Deep Convolutional Neural Networks ◽  
Comprehensive Overview ◽  
Image Objects ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Methodological Considerations

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.

scholarly journals Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping

2019 ◽  
Vol 11 (5) ◽  
pp. 503 ◽  
Author(s):  
Sachit Rajbhandari ◽  
Jagannath Aryal ◽  
Jon Osborn ◽  
Arko Lucieer ◽  
Robert Musk
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Classification Accuracy ◽  
Forest Type ◽  
Rule Based ◽  
Object Based Image Analysis ◽  
Type Mapping ◽  
Object Based ◽  
Geographic Object

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy.

Gully mapping using geographic object-based image analysis: A case study at catchment scale in the Brazilian Cerrado

2020 ◽  
Vol 20 ◽  
pp. 100399
Author(s):  
Alex Garcez Utsumi ◽  
Teresa Cristina Tarlé Pissarra ◽  
David Luciano Rosalen ◽  
Marcílio Vieira Martins Filho ◽  
Luiz Henrique Silva Rotta
Keyword(s):  
Image Analysis ◽  
Brazilian Cerrado ◽  
Catchment Scale ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

scholarly journals A COGNITIVE APPROACH TO TEACHING A GRADUATE-LEVEL GEOBIA COURSE

2016 ◽  
Vol III-6 ◽  
pp. 3-7
Author(s):  
Raechel A. Bianchetti
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Cognitive Factors ◽  
Visual Interpretation ◽  
Cognitive Approach ◽  
Course Materials ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Computer Based

Remote sensing image analysis training occurs both in the classroom and the research lab. Education in the classroom for traditional pixel-based image analysis has been standardized across college curriculums. However, with the increasing interest in Geographic Object-Based Image Analysis (GEOBIA), there is a need to develop classroom instruction for this method of image analysis. While traditional remote sensing courses emphasize the expansion of skills and knowledge related to the use of computer-based analysis, GEOBIA courses should examine the cognitive factors underlying visual interpretation. This current paper provides an initial analysis of the development, implementation, and outcomes of a GEOBIA course that considers not only the computational methods of GEOBIA, but also the cognitive factors of expertise, that such software attempts to replicate. Finally, a reflection on the first instantiation of this course is presented, in addition to plans for development of an open-source repository for course materials.

scholarly journals A COGNITIVE APPROACH TO TEACHING A GRADUATE-LEVEL GEOBIA COURSE

2016 ◽  
Vol III-6 ◽  
pp. 3-7
Author(s):  
Raechel A. Bianchetti
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Cognitive Factors ◽  
Visual Interpretation ◽  
Cognitive Approach ◽  
Course Materials ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Computer Based

Remote sensing image analysis training occurs both in the classroom and the research lab. Education in the classroom for traditional pixel-based image analysis has been standardized across college curriculums. However, with the increasing interest in Geographic Object-Based Image Analysis (GEOBIA), there is a need to develop classroom instruction for this method of image analysis. While traditional remote sensing courses emphasize the expansion of skills and knowledge related to the use of computer-based analysis, GEOBIA courses should examine the cognitive factors underlying visual interpretation. This current paper provides an initial analysis of the development, implementation, and outcomes of a GEOBIA course that considers not only the computational methods of GEOBIA, but also the cognitive factors of expertise, that such software attempts to replicate. Finally, a reflection on the first instantiation of this course is presented, in addition to plans for development of an open-source repository for course materials.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

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