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GeoSay: A geometric saliency for extracting buildings in remote sensing images

Xia, Gui-Song and Huang, Jin and Xue, Nan and Lu, Qikai and Zhu, Xiao Xiang (2019) GeoSay: A geometric saliency for extracting buildings in remote sensing images. Computer Vision and Image Understanding (186), pp. 37-47. Elsevier. DOI: 10.1016/j.cviu.2019.06.001 ISSN 1077-3142

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Official URL: https://www.sciencedirect.com/science/article/pii/S1077314219300918#!

Abstract

Automatic extraction of buildings in remote sensing images is an important but challenging task and finds many applications in different fields such as urban planning, navigation and so on. This paper addresses the problem of buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS) images, whose spatial resolution is often up to half meters and provides rich information about buildings. Based on the observation that buildings in VHSR-RS images are always more distinguishable in geometry than in texture or spectral domain, this paper proposes a geometric building index (GBI) for accurate building extraction, by computing the geometric saliency from VHSR-RS images. More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images. The resulting GBI is finally measured by integrating the derived geometric saliency of buildings. Experiments on three public and commonly used datasets demonstrate that the proposed GBI achieves the state-of-the-art performance and shows impressive generalization capability. Additionally, GBI preserves both the exact position and accurate shape of single buildings compared to existing methods.

Item URL in elib:https://elib.dlr.de/134085/
Document Type:Article
Title:GeoSay: A geometric saliency for extracting buildings in remote sensing images
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Xia, Gui-Songguisong.xia (at) whu.edu.cnUNSPECIFIED
Huang, JinWuhan UniversityUNSPECIFIED
Xue, NanWuhan UniversityUNSPECIFIED
Lu, QikaiElectronic Information School, Wuhan UniversityUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:September 2019
Journal or Publication Title:Computer Vision and Image Understanding
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1016/j.cviu.2019.06.001
Page Range:pp. 37-47
Publisher:Elsevier
ISSN:1077-3142
Status:Published
Keywords:Building detection Geometric saliency Junction Remote sensing image
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Rösel, Anja
Deposited On:13 Feb 2020 10:05
Last Modified:13 Feb 2020 10:05

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