Aerial Photogrammetry and Object-based Image Analysis for Bridge Mapping: A Case Study on Bintan Bridge, Riau Islands, Indonesia

2019 ◽  
Author(s):  
Husnul Kausarian ◽  
Muhammad Zainuddin Lubis ◽  
. Primawati ◽  
Dewandra Bagus Eka Putra ◽  
Adi Suryadi ◽  
...  
Keyword(s):  
Image Analysis ◽  
Object Based Image Analysis ◽  
Aerial Photogrammetry ◽  
Object Based

scholarly journals Offshore benthic habitat mapping based on object-based image analysis and geomorphometric approach. A case study from the Slupsk Bank, Southern Baltic Sea

2021 ◽  
Vol 801 ◽  
pp. 149712 ◽  
Author(s):  
Lukasz Janowski ◽  
Radoslaw Wroblewski ◽  
Janusz Dworniczak ◽  
Mateusz Kolakowski ◽  
Karolina Rogowska ◽  
...  
Keyword(s):  
Image Analysis ◽  
Baltic Sea ◽  
Habitat Mapping ◽  
Benthic Habitat ◽  
Southern Baltic Sea ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Southern Baltic

scholarly journals Application of Object Based Image Analysis for Glacial Cirques Detection. Case Study: The Ţarcu Mountains (Southern Carpathians)

2011 ◽  
Vol X (1) ◽  
pp. 20-26 ◽  
Author(s):  
Florina Ardelean ◽  
Marcel Török-Oance ◽  
Petru Urdea ◽  
Alexandru Onaca
Keyword(s):  
Image Analysis ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Southern Carpathians

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 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
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