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 129 * I, | s, :i ‘ V*| I ■ ' »♦ % I. 1 *: ; 'w .v r (a) Detected Buildings /- r ^ ^ •- Jv *1 l'rfv? i -Ti W*’ • * .T^T 4 ^ “ • **— % % (b) Ground Truth ■■I ‘ v VI : * .* ' ♦*! *•* ' ■^JT V */ .N. r (c) Horizontal True Positives (HTP) t; ■;% s? i: V r ♦♦ (d) Horizontal False Positives (HFP) «. / f f •• \ 4 (e) Horizontal False Negatives (HFN) Figure 3: Horizontal Qualitative Evaluation: The recognition-driven process efficiently detects, in an unsupervised manner, scene buildings and recovers their 3D geometry. gable-type one ($1.4) and if all are zero we have a flat one ($1.1). The platform and the gambrel roof types can not be modeled but can be easily derived in cases where the fit energy metric is as sumed on local minima. The platform one (l>i, 2 ), for instance, is the case where all angles have been recovered with small val ues and a search around their intersection point will estimate the dimensions of the rectangular-shape box above main roof plane Pm. With the aforementioned formulations, instead of searching for the best among ixj (e.g. 5x6 = 30) models, their hierarchical grammar and the appropriate defined energy terms (detailed in the following section) are able to cut down effectively the solutions space. from the grouping criteria. The simplest possible approach would involve the Mumford-Shah approach that aims at separating the means between the two classes. Above equation can be straight forwardly extended in order to deal with other optical or radar data like for example in cases where multi- or hyper-spectral re mote sensing data are available. Furthermore, instead of relying only on the results of an uncon strained evolving surface, we are forcing our output segments to inherit their 2D shape from our prior models. Thus, instead of evolving an arbitrary surface we evolve selected geometric shapes and the 2D prior-based segmentation energy term takes the fol lowing form: 3 MULTIPLE 3D PRIORS IN COMPETITION EXTRACTING MULTIPLE OBJECTS Let us consider an image (X) and the corresponding digital eleva tion map (Pi). In such a context, one has to separate the desired for extraction objects from the background (natural scene) and, then, determine their geometry. The first segmentation task is ad dressed through the deformation of a initial surface —► 1Z + that aims at separating the natural components of the scene from the man-made parts. Assuming that one can establish correspon dences between the pixels of the image and the ones of the DEM, the segmentation can be solved in both spaces through the use of regional statistics. In the visible image we would expect that buildings are different from the natural components of the scene. In the DEM, one would expect that man-made structures will ex hibit elevation differences from their surroundings. Following the formulations of (Karantzalos and Paragios, 2009), these two assumptions can be used to define the following segmentation function Eseg (0) J |Vc/>(x)| dx + + P J Jq H € (4>) r obj (J(x)) [ H e {<f>) r obj (7T(x)) Jci + [1 - He(</>)] r bg (T(x)) dx + [1 - He(4>)\ r b g (Pi(x)) dx (1) where H is the Heaviside, r ob j and r bg are object and background positive monotonically decreasing data-driven functions driven E2d{4>, Ti, L) He(<f>{x)) — H e (4>i (Ti(x))) Xi(L(x))d,x + J \ 2 Xm(L(x))dx + p'^2 J IVL(x)|dx i=l (2) with the two parameters A, p > 0 and the A>dimensional label ing formulation able for the dynamic labeling of up to m = 2 k regions. In this way, during optimization the number of selected regions m = 2 k depends on the number of the possible building segments according to <j> and thus the (¿-dimensional labeling function L obtains incrementally multiple instances. It should be, also, men tioned that the initial pose of the priors are not known. Such a formulation E seg + E2D allows data with the higher spatial res olution to constrain properly the footprint detection in order to achieve the optimal spatial accuracy. Furthermore, it solves seg mentation simultaneously in both spaces (image and DEM) and addresses fusion in a natural manner. 3.1 Grammar-based Object Reconstruction In order to determine the 3D geometry of the buildings, one has to estimate the height of the structure with respect to the ground and the orientation angles of the roof components i.e. five un known parameters: the building’s main height hm which is has a constant value for every building and the four angles u> of the

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