CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: COMPETING 3D PRIORS FOR OBJECT EXTRACTION IN REMOTE SENSING DATA Konstantinos Karantzalos and Nikos Paragios

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 COMPETING 3D PRIORS FOR OBJECT EXTRACTION IN REMOTE SENSING DATA Konstantinos Karantzalos and Nikos Paragios Ecole Centrale de Paris Grande Voie des Vignes, 92295 Chatenay-Malabry, France {konstantinos.karantzalos, nikos.paragios} @ecp.fr http://users.ntua.gr/karank/Demos.html Commission III KEY WORDS: Computer Vision, Pattern Recognition, Variational Methods, Model-Based, Evaluation, Voxel-Based ABSTRACT: A recognition-driven variational framework was developed for automatic three dimensional object extraction from remote sensing data. The essence of the approach is to allow multiple 3D priors to compete towards recovering terrain objects’ position and 3D geometry. We are not relying, only, on the results of an unconstrained evolving surface but we are forcing our output segments to inherit their 3D shape from our prior models. Thus, instead of evolving an arbitrary surface we evolve the selected geometric shapes. The developed algorithm was tested for the task of 3D building extraction and the performed pixel- and voxel-based quantitative evaluation demonstrate the potentials of the proposed approach. 1 INTRODUCTION Although, current remote sensing sensors can provide an updated and detailed source of information related to terrain analysis, the lack of automated operational procedures regarding their process ing impedes their full exploitation. By using standard techniques based, mainly, on spectral properties, only the lower resolution earth observation data can be effectively classified. Recent auto mated approaches are not, yet, functional and mature enough for supporting massive processing on multiple scenes of high- and very high resolution data. On the other hand, modeling urban and peri-urban environments with engineering precision, enables people and organizations in volved in the planning, design, construction and operations life- cycle, in making collective decisions in the areas of urban plan ning, economic development, emergency planning, and security. In particular, the emergence of applications like games, naviga tion, e-commerce, spatial planning and monitoring of urban de velopment has made the creation and manipulation of 3D city models quite valuable, especially at large scale. In this perspective, optimizing the automatic information extrac tion of terrain features/objects from new generation satellite data is of major importance. For more than a decade now, research efforts are based on the use of a single image, stereopairs, multi ple images, digital elevation models (DEMs) or a combination of them. One can find in the literature several model-free or model- based algorithms towards 2D and 3D object extraction and recon struction [ (Hu et al., 2003),(Baltsavias, 2004),(Suveg and Vossel- man, 2004),(Paparoditis et al., 2006),(Drauschke et al., 2006),(Rot- tensteiner et al., 2007),(Sohn and Dowman, 2007),(Verma et al., 2006),(Lafarge et al., 2007),(Karantzalos and Paragios, 2009) and the references therein]. Despite this intensive research, we are, still, far from the goal of the initially envisioned fully automatic and accurate reconstruction systems (Brenner, 2005),(Zhu and Kanade (Eds.), July, 2008),(Mayer, 2008). Processing remote sensing data, still, poses several challenges. In this paper, we extend our recent 2D prior-based formulations (Karantzalos and Paragios, 2009) aiming at tackling the prob lem of automatically and accurately extracting 3D terrain objects (a) Satellite Image (b) Ground Truth (c) DEM (d) Extracted 3D Buildings (e) Reconstructed Scene Figure 1: 3D Building Extraction through Competing 3D Priors from optical and height data. Multiple 3D competing priors are considered transforming reconstruction to a labeling and an esti mation problem. In such a context, we fuse images and DEMs towards recovering a 3D prior model. We are experimenting with buildings but, similarly, any other terrain object can be modeled. Our formulation allows data with the higher spatial resolution to constrain properly the footprint detection in order to achieve the optimal spatial accuracy (Figure 1). Therefore, we are proposing a variational functional that encodes a fruitful synergy between observations and multiple 3D grammar-based models. Our mod els refer to a grammar, which consists of typologies of 3D shape priors (Figure 2). In such a context, firstly one has to select the 127

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