A Region-Line Primitive Association Framework for Object-Based Remote Sensing Image Analysis

2016 ◽  
Vol 82 (2) ◽  
pp. 149-159 ◽  
Author(s):  
Min Wang ◽  
Jie Wang
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Object Based ◽  
Region Line

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

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 The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

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