scholarly journals A Study on Closed Region Extraction for building extraction from Simulated High Resolution Satellite Imagery.

1998 ◽  
Vol 37 (5) ◽  
pp. 4-12
Author(s):  
Zhongchao SHI ◽  
Ryosuke SHIBASAKI
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Building Extraction ◽  
Closed Region ◽  
Region Extraction ◽  
High Resolution Satellite Imagery

scholarly journals BUILDING FOOTPRINT EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGERY USING SEGMENTATION

2018 ◽  
Vol XLII-5 ◽  
pp. 123-128 ◽  
Author(s):  
S. Khatriker ◽  
M. Kumar
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Satellite Imagery ◽  
Smart Cities ◽  
Building Extraction ◽  
Rule Based ◽  
Segmented Image ◽  
High Resolution Imagery ◽  
Building Footprint ◽  
High Resolution Satellite Imagery

<p><strong>Abstract.</strong> Identification and mapping of urban features such as buildings and roads are an important task for cartographers and urban planners. High resolution satellite imagery supports the efficient extraction of manmade objects. For the planning and designing of Smart cities, building footprint information is an essential component, and geospatial technologies helps in creating this large mass of data inputs for designing and planning of smart cities. In this study segmentation approach is followed for building extraction. For extraction of buildings especially from the high resolution imagery, number of various semiautomatic and automatic methods have been developed till date to reduce the time and efforts required in manual building mapping. In this study, two semiautomatic image segmentation techniques are used for building extraction from high resolution imagery using algorithms- Multi-resolution segmentation and Rule based feature extraction, which are applied on Worldview 2 (2010) imagery of Dehradun area. The segmented image were further classified to extract buildings from the segmented image features. The study identify the usefulness of both the methods in building extraction and finds the optimum set of rules for extracting buildings from high resolution data sets. The True Positive Rate using Rule based feature extraction is 88.11<span class="thinspace"></span>% compared to 85.46<span class="thinspace"></span>% from Multi-resolution segmentation algorithm. The False Negative Rate (FNR) of Multi-resolution segmentation algorithm (16.5<span class="thinspace"></span>%.) is very less compared to Rule based feature extraction (67.5<span class="thinspace"></span>%). In the study the buildings were extracted with the accuracy of 88.9<span class="thinspace"></span>%.</p>

scholarly journals Hybrid method for building extraction in vegetation-rich urban areas from very high-resolution satellite imagery

2017 ◽  
Vol 11 (03) ◽  
pp. 1
Author(s):  
Ajith S. Jayasekare ◽  
Rohan Wickramasuriya ◽  
Mohammad-Reza Namazi-Rad ◽  
Pascal Perez ◽  
Gaurav Singh
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Hybrid Method ◽  
Urban Areas ◽  
Building Extraction ◽  
High Resolution Satellite Imagery ◽  
Very High

Automatic extraction of man-made objects from high-resolution satellite imagery by information fusion

2005 ◽  
Author(s):  
◽  
Xiaoying Jin
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Contextual Information ◽  
Automatic Extraction ◽  
Building Extraction ◽  
Geospatial Information ◽  
University Of Missouri ◽  
Sensing Applications ◽  
High Resolution Satellite Imagery ◽  
The University

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recently available high-resolution commercial satellite imagery provides an important new data source for remote sensing applications. Automated feature extraction (AFE) techniques can assist human analysts by rapidly locating geospatial information and have the potential to significantly reduce the amount of time to process and analyze geospatial data. In this research, we have designed and developed systems for automatic extraction of man-made objects (roads, buildings and vehicles) from high-resolution satellite imagery. We conclude that AFE can be greatly enriched and improved by multiinformation fusion and/or multi-cue integration. For road extraction and building extraction respectively, multiple detectors were developed and the extraction performance was greatly improved using multi-detector fusion from different information sources. For vehicle detection, a GIS road vector layer was used to incorporate contextual information and an implicit vehicle model including spectral and spatial characteristics was learned by a morphological shared-weight neural network. An important characteristic of our research on road and building extraction is that our extraction strategies are fully automated with only a few preset parameters. Compared with related research in these areas, the performance evaluations of our extraction systems are among the highest statistical values reported in literature thus far.

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