An experimental system for the integration of GIS data in knowledge-based image analysis for remote sensing of agriculture

1993 ◽  
Vol 7 (3) ◽  
pp. 247-262 ◽  
Author(s):  
C. Kontoes ◽  
G. G. Wilkinson ◽  
A. Burrill ◽  
S. Goffredo ◽  
J. Mégier
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Experimental System ◽  
Knowledge Based ◽  
Gis Data

Concluding comments

1988 ◽  
Vol 324 (1579) ◽  
pp. 471-472
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Analysis ◽  
Error Rates ◽  
Knowledge Based ◽  
Diverse Application ◽  
Human Capability

This has been a most stimulating meeting, not least of all because of the healthy mix of people attending. We have heard lively discussions between users and researchers, both in the formal sessions and during the breaks. Representation from widely diverse application areas and examples taken from a variety of images demonstrate that we represent a unified community of interest. Many of us sense that this meeting has occurred at a watershed in the development of image-analysis as applied to applications as demanding as remote sensing. With an acute awareness of the limitations of pixel-based image-processing, it is easy to see the appeal of knowledge-based techniques that promise to build in some of the richness of human capability. If this can be achieved without including human limitations, such as the tendency to subjectivity and increasing error rates during repetitive operational tasks, then so much the better.

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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