An Efficient Parallel Algorithm for Graph-Based Image Segmentation

Wassenberg, Jan; Middelmann, Wolfgang; Sanders, Peter

doi:10.1007/978-3-642-03767-2_122

Cited by 45 publications

(27 citation statements)

References 13 publications

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“…Wassenberg et al (2009) proposed a parallelization method for graph-based image segmentation that does not truncate objects located at partition boundaries. Considering that remotely sensed data for entire forests is usually quite large, the potential to parallelize the segmentation process is a significant advantage.…”

Section: Segmentation Framework and Perspectivementioning

confidence: 99%

A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

Strîmbu

2015

ISPRS Journal of Photogrammetry and Remote Sensing

116

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a b s t r a c tThis work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures.The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m 2 and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m 2 .More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent.

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Section: Segmentation Framework and Perspectivementioning

confidence: 99%

A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

Strîmbu

2015

ISPRS Journal of Photogrammetry and Remote Sensing

116

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“…One example are graph based methods like in (Wassenberg et al, 2009). Though at this stage it is important that, as mentioned above, spatial features are used.…”

Section: Local Segmentationmentioning

confidence: 99%

Improving Active Queries With a Local Segmentation Step and Application to Land Cover Classification

Wuttke

Middelmann

Stilla³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Active queries is an active learning method used for classification of remote sensing images. It consists of three steps: hierarchical clustering, dendrogram division, and active label selection. The goal of active learning is to reduce the needed amount of labeled data while preserving classification accuracy. We propose to apply local segmentation as a new step preceding the hierarchical clustering. We are using the SLIC (simple linear iterative clustering) algorithm for dedicated image segmentation. This incorporates spatial knowledge which leads to an increased learning rate and reduces classification error. The proposed method is applied to six different areas of the Vaihingen dataset.

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“…This method has been extended successfully to many domains, including for use in parallel segmentation [17] and to tracking fiducial tags [11]. Given the joint cloud V = [v 1 , · · · , v i ] whose construction is specified above, we consider the problem of segmenting (or clustering) this set into disjoint subsets…”

Section: Graph-based Segmentationmentioning

confidence: 99%

Graph-based segmentation for colored 3D laser point clouds

Strom

Richardson

Olson

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

138

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Abstract-We present an efficient graph-theoretic algorithm for segmenting a colored laser point cloud derived from a laser scanner and camera. Segmentation of raw sensor data is a crucial first step for many high level tasks such as object recognition, obstacle avoidance and terrain classification. Our method enables combination of color information from a wide field of view camera with a 3D LIDAR point cloud from an actuated planar laser scanner. We extend previous work on robust camera-only graph-based segmentation to the case where spatial features, such as surface normals, are available. Our combined method produces segmentation results superior to those derived from either cameras or laser-scanners alone. We verify our approach on both indoor and outdoor scenes.

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An Efficient Parallel Algorithm for Graph-Based Image Segmentation

Cited by 45 publications

References 13 publications

A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

Improving Active Queries With a Local Segmentation Step and Application to Land Cover Classification

Graph-based segmentation for colored 3D laser point clouds

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