Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities-Reference-Cited by-同舟云学术

Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities

Published:2018-01-18 Issue:2 Volume:55 Page:159-182
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Chen Gang¹^ORCID,Weng Qihao²^ORCID,Hay Geoffrey J.³^ORCID,He Yinan¹^ORCID

Affiliation:

1. Laboratory for Remote Sensing and Environmental Change (LRSEC), Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA

2. Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA

3. Department of Geography, University of Calgary, Calgary AB T2N 1N4, Canada

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2018.1426092

Reference125 articles.

1. Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas

2. Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective

3. Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification

4. Baraldi, A. 2017. “Pre-processing, Classification and Semantic Querying of Large-scale Earth Observation Spaceborne/Airborne/Terrestrial Image Databases: Process and Product Innovations.” Unpublished PhD Thesis. University of Naples “Federico II”, Italy, pp 519.

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