Next Article in Journal
Technique for Concurrent Internal Calibration during Data Acquisition for SAR Systems
Previous Article in Journal
Confusion Matrices Help Prevent Reader Confusion: Reply to Bechtel, B., et al. A Weighted Accuracy Measure for Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment, Remote Sens. 2019, 11, 2420
Previous Article in Special Issue
Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning
Open AccessEditorial

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

by 1 and 2,3,*
1
Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, 2108-11, Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan
2
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
3
Signal Processing in Earth Observation, Technical University of Munich, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1772; https://doi.org/10.3390/rs12111772
Received: 25 May 2020 / Accepted: 29 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)
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. View Full-Text
Keywords: GEOBIA; object-based image analysis; high-spatial-resolution; image segmentation parameter optimization GEOBIA; object-based image analysis; high-spatial-resolution; image segmentation parameter optimization
Show Figures

Figure 1

MDPI and ACS Style

Johnson, B.A.; Ma, L. Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities. Remote Sens. 2020, 12, 1772. https://doi.org/10.3390/rs12111772

AMA Style

Johnson BA, Ma L. Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities. Remote Sensing. 2020; 12(11):1772. https://doi.org/10.3390/rs12111772

Chicago/Turabian Style

Johnson, Brian A.; Ma, Lei. 2020. "Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities" Remote Sens. 12, no. 11: 1772. https://doi.org/10.3390/rs12111772

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop