Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis

2012 ◽  
Vol 15 ◽  
pp. 1-6 ◽  
Author(s):  
Elisabeth A. Addink ◽  
Frieke M.B. Van Coillie ◽  
Steven M. De Jong
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Special Issue

scholarly journals Introduction to the Special Issue “Uncertainty in Remote Sensing Image Analysis”

2018 ◽  
Vol 10 (12) ◽  
pp. 1975
Author(s):  
Alfred Stein ◽  
Yong Ge ◽  
Inger Fabris-Rotelli
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Special Issue

Images obtained from satellites are of an increasing resolution. [...]

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.

Weighted model-based clustering for remote sensing image analysis

2009 ◽  
Vol 14 (1) ◽  
pp. 125-136 ◽  
Author(s):  
Joseph W. Richards ◽  
Johanna Hardin ◽  
Eric B. Grosfils
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Model Based Clustering ◽  
Model Based ◽  
Weighted Model

Airborne remote sensing in forestry: Sensors, analysis and applications

2000 ◽  
Vol 76 (6) ◽  
pp. 859-876 ◽  
Author(s):  
Douglas J. King
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Key Words ◽  
Analytical Techniques ◽  
Remote Sensing Image ◽  
Digital Cameras ◽  
Airborne Remote Sensing ◽  
Hyperspectral Sensors ◽  
Imaging Characteristics ◽  
Laser Remote Sensing

This paper discusses the aspects of airborne remote sensing that are critical to forestry applications, the imaging characteristics of the most common sensors currently available, and analytical techniques that make use of the great amount of information content in airborne imagery. As the first paper in the CIF technical meeting to which this issue of the Forestry Chronicle is devoted, the paper is intended to provide an overview and context for subsequent papers and not a presentation of specific research methods or results. Key words: airborne remote sensing, forestry, photography, digital cameras, hyperspectral sensors, radar, laser remote sensing, image analysis

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