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Results 1-8 of 8 (Search time: 0.002 seconds).
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  • An improved Bayesian nonparametric mixture model to fusing both panchromatic and multispectral images for classification

    • Authors: Mao, T.;  Advisor: -;  Participants: - (2016)

  • In this paper, we present an improved nonparametric Bayesian model based on a generalized metaphor of Chinese restaurant franchise (gCRF), which can take advantage of both panchromatic and multispectral images to obtain a classification map. There are two drawbacks in the gCRF when it is used to fuse panchromatic and multispectral image for classification, first, since superpixels which are obtained using other segmentation algorithm are considered as basic analysis units instead of pixels in the gCRF, the quality of final classification result depends on the calibre of over-segmentation map. Second, when classify PAN and MS image using the gCRF, semantic segments extracted from PAN image are sharing with MS image and then they are allocated clustering labels using MS image which is...
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  • Detection, segmentation and localization of individual trees from mms point cloud data

    • Authors: Weinmann, M.;  Advisor: -;  Participants: - (2016)

  • In this paper, we address the extraction of objects from 3D point clouds acquired with mobile mapping systems. More specifically, we focus on the detection of tree-like objects, a subsequent segmentation of individual trees and a localization of the respective trees. Thereby, the detection of tree-like objects is achieved via a binary point-wise classification based on geometric features, which categorizes each point of the 3D point cloud into either tree-like objects or non-tree-like objects. The subsequent segmentation and localization of individual trees is carried out by applying a 2D projection and a mean shift segmentation on a downsampled version of that part of the original 3D point cloud which represents all tree-like objects, and it also involves a segment-based shape anal...
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  • 3D semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction

    • Authors: Blomley, R.;  Advisor: -;  Participants: - (2016)

  • The semantic labeling of 3D point clouds acquired via airborne laser scanning typically relies on the use of geometric features. In this paper, we present a framework considering complementary types of geometric features extracted from multi-scale, multi-type neighborhoods to describe (i) the local 3D structure for neighborhoods of different scale and type and (ii) how the local 3D structure behaves across different scales and across different neighborhood types. The derived features are provided as input for several classifiers with different learning principles in order to show the potential and limitations of the proposed geometric features with respect to the classification task. To allow a comparison of the performance of our framework to the performance of existing and future ...
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  • Detecting atlantic forest patches applying geobia and data mining techniques

    • Authors: Girolamo Neto, C.D.;  Advisor: -;  Participants: - (2016)

  • Brazilian Atlantic Forest is one of the most devastated tropical forests in the world. Considering that approximately only 12% of its original extent still exists, studies in this area are highly relevant. In this context, this study maps the land cover of Atlantic Forest within the Protected Area of ‘Macaé de Cima’, in Rio de Janeiro State, Brazil, combining GEOBIA and data mining techniques on an OLI/Landsat-8 image. The methodology proposed in this work includes the following steps: (a) image pan-sharpening; (b) image segmentation; (c) feature selection; (d) classification and (e) model evaluation. A total of 15 features, including spectral information, vegetation indices and principal components were used to distinguish five patterns, including Water, Natural forest, Urban area,...
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  • From classification results to topographic maps

    • Authors: Höhle, Joachim;  Advisor: -;  Participants: - (2016)

  • The classification of high resolution multispectral aerial imagery enables very high thematic accuracies when machine learning methods are applied. The use of classification results as topographic maps requires cartographic enhancement and checking of the geometric accuracy. Urban areas are of special interest. The conversion of the classification result into topographic maps of high thematic and geometric quality is subject of this contribution. After reviewing the existing literature on this topic a methodology is presented. It has the goal to achieve high cartographic quality and geometric accuracy for buildings and other topographic objects. The suggested methodology for improving the classification results is described. With the ISPRS data set of the ‘2D labelling contest’ a la...
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  • Synergy between aerial imagery and low density point cloud for automated image classification and point cloud densification

    • Authors: Mohammed Badawy, Hani;  Advisor: -;  Participants: - (2016)

  • In this paper a synergy scheme between aerial imagery and sparse LIDAR point clouds is proposed for an automated aerial image classification. In this scheme, a point cloud and an image are chosen for a certain urban area. The point cloud is automatically classified into buildings, vegetation and roads using PCA and intensity variation. Afterwards, a projection of the point cloud into an image is obtained, such that it is registered with the aerial image. The aerial image classifier is trained with the LIDAR classification result to generate an automated classifier for aerial images. The classifier is tested with another image to demonstrate its accuracy. Another benefit of the synergy proposed is to densify the planar patches of the low density point cloud using the segmented aerial...
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  • RSOBIA - A new OBIA Toolbar and Toolbox in ArcMap 10.x for Segmentation and Classification

    • Authors: Bas, T.P. le;  Advisor: -;  Participants: - (2016)

  • A need was therefore seen for OBIA to be integrated into GIS. ESRI’s ArcMap is currently used by millions of people daily worldwide and could benefit from access to OBIA. Easy usage and access is essential. A toolbar for push button control was envisaged and RSOBIA (Remote sensing Object Based Image Analysis) was created. The toolbar is easy to use and requires very little training. For the slightly higher level user a toolbox has also been created, so that the RSOBIA functions can be incorporated into other programming models and allows more complex functions to be created.
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  • Potential of multi-temporal oblique airborne imagery for structural damage assessment

    • Authors: Vetrivel, A.;  Advisor: -;  Participants: - (2016)

  • Quick post-disaster actions demand automated, rapid and detailed building damage assessment. Among the available technologies, post-event oblique airborne images have already shown their potential for this task. However, existing methods usually compensate the lack of pre-event information with aprioristic assumptions of building shapes and textures that can lead to uncertainties and misdetections. However, oblique images have been already captured over many cities of the world, and the exploitation of pre- and post-event data as inputs to damage assessment is readily feasible in urban areas. In this paper, we investigate the potential of multi-temporal oblique imagery for detailed damage assessment focusing on two methodologies: the first method aims at detecting severe structural ...
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