Paper Info
Title
Regular Shape Similarity Index: A Novel Index for Accurate Extraction of Regular Objects From Remote Sensing Images
Abstract
It still remains a big challenge to accurately identify the geospatial objects with well-regulated outlines within remote sensing (RS) images such as residential buildings, factory storage buildings, highways, local roads, cars, and planes. In this paper, a novel spatial feature index, which is named regular shape similarity index (RSSI), is defined to address the challenge. It represents the ratio between the area of an object and its minimum bounding shape area. The application of RSSI in identifying objects with different shapes is discussed, and its capability is found to be a great supplement to the existing spatial feature hierarchy. An approach combining RSSI with object-based image analysis (OBIA) technology is proposed for image object extraction. A Web service for RSSI calculation is developed and integrated into a Web OBIA system. In the system, four experiments extracting factory storage buildings, residential buildings, roads, and planes, respectively, are conducted on three large-scale high-resolution RS images. In each experiment, two tests, i.e., one using traditional spatial features and the other using RSSI, are performed and compared. The results show that RSSI improves the accuracy of regular object extraction.
Year
DOI
Venue
2015
10.1109/TGRS.2014.2382566
IEEE T. Geoscience and Remote Sensing
Keywords
Field
DocType
geographical information system,remote sensing,object-based image analysis (obia),web obia system,regular shape similarity index (rssi),image object extraction,spatial feature hierarchy,geospatial object identification,web services,object-based image analysis,remote sensing image,rssi,factory storage building extraction,remote sensing (rs),web service,geoprocessing web service,residential buildings,feature extraction,regular object extraction,geophysical image processing,regular shape similarity index,geospatial feature,plane extraction,object detection,road extraction,spatial feature index,geospatial analysis,shape,object recognition,indexes
Geospatial analysis,Computer vision,Remote sensing,Feature extraction,Artificial intelligence,Web service,Hierarchy,Mathematics,Cognitive neuroscience of visual object recognition,Bounding overwatch
Journal
Volume
Issue
ISSN
53
7
0196-2892
Citations 
PageRank 
References 
5
0.45
21
Authors
6
Name
Order
Citations
PageRank
Ziheng Sun18215.31
Hui Fang2234.02
Meixia Deng36110.82
Aijun Chen411911.17
Peng Yue537434.25
Liping Di681198.92