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:: COMPLEX SCENE ANALYSIS IN URBAN AREAS BASED ON AN ENSEMBLE CLUSTERING METHOD APPLIED ON LIDAR DATA P. Ramzi, F. Samadzadegan

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3A/V4 — Paris, France, 3-4 September, 2009 Here, the membership degree h for every instance x, to cluster j, is produced based on the Euclidean distance d: d(x n /jj) where fj. e <g d = cluster center. At each iteration, the boost-clustering algorithm clusters data points that were hard to cluster in previous iterations. An important issue to be addressed here and that is the cluster correspondence problem between the clustering results of different iterations (Frossyniotis et al., 2004). 2.2 Feature Extraction The first step in every clustering process is to extract the feature image bands. These features must contain useful information to discriminate between different regions of the surface. In our experiment we have used two types of features: - The filtered first pulse range image using gradient - Opening filtered last pulse range image By our experiments, these two features have enough infonnation to extract our objects of interest. The normalized difference of the first and last pulse range images (NDDI) is usually used as the major feature band for discrimination of the vegetation pixels from the others. However, building boundaries also show a large value in this image feature. It is because when the laser beam hits the exposed surface it will have a footprint with a size in the range of 15-30 cm or more. So, if the laser beam hits the edge of a building, then part of the beam footprint will be reflected from the top roof of the building and the other part might reach the ground (Alharthy and Bethel, 2002). The high gradient response on building edges was utilized to filter out the NDDI image using equation 6. NDDI = FPR-LPR FPR + LPR (6) if gradient > threshold, then (FPR-LPR) = 0.0 where FPR = first-pulse range image data LPR = last-pulse range image data The gradient of an image is calculated using equation 7: G(image) = ^G x (image) 2 + G y (image) 2 where G x = gradient operators in x direction. G y = gradient operators in y direction. (7) The morphology Opening operator is utilized to filter elevation space. This operator with a flat structuring element eliminates the trend surface of the terrain. The main problem of using this filter is to define the proper size of the structuring element which should be big enough to cover all 3D objects which can be found on the terrain surface. The Opening operation is defined by: AoB = (AQB)®B (8) where A ® B - jx: | {¿ x n^)c= a] (9) is the morphological Dilation of set A with structure element B. And AQB = {x | B x <z A) (10) is the morphological Erosion of set A with structure element B (Gonzalez and Woods, 2006). 2.3 Quality Analysis Comparative studies on clustering algorithms are difficult due to lack of universally agreed upon quantitative performance evaluation measures (Jain et al., 1999). Many similar works in the clustering area use the classification error as the final quality measurement; so in this research, we adopt a similar approach. Here, we use error matrix as main evaluation method of interpretation result. Each column of this matrix indicates the instances in a predicted class. Each row represents the instances in an actual class. All the diagonal variants refer to the correct interpreted numbers of different classes found in reality. Some measures can be derived from the error matrix, such as producer accuracy, user accuracy and overall accuracy (Liu et al, 2007). Producer Accuracy (PA) is the probability that a sampled unit in the image is in that particular class. User Accuracy (UA) is the probability that a certain reference class has also been labelled that class. Producer accuracy and user accuracy measures of each class indicate the interpretability of each feature class. We can see the producer accuracy and user accuracy of all the classes in the measures of “producer overall accuracy” and “user overall accuracy”. PA, f N i i} ( N i i Ì —Li- * 100% . , UA,= — 1 N .i 1 KJ : ! 00% (ID where N. = (i,j)th entry in confusion matrix N j = the sum of all columns for row i N is the sum of all rows for column i. “Overall accuracy” considers all the producer accuracy and user accuracy of all the feature classes. Overall accuracy yields one number of the whole error matrix. It‘s the sum of correctly classified samples divided by the total sample number from user set and reference set (Liu et al, 2007). k ± N u OA = -7——— ^ * 100% ( 12 ) k /=i /=1 Another factor can be also extracted from confusion matrix to evaluate the quality of classification algorithms, which is K-

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