Automatic extraction of wind erosion obstacles by integration of GIS data, DSM and stereo images

Zhang, Y.; Heipke, Christian; Butenuth, Matthias; Hu, Xiangyun

doi:10.1080/01431160500406896

Cited by 6 publications

(8 citation statements)

References 13 publications

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“…Similarly, the extraction of trees has been investigated, see Brandtberg and Walter (1998) for an example suitable for forestry. Zhang et al (2006) have presented an approach for the extraction of wind erosion obstacles in the open landscape. In contrary, the extraction of field boundaries has not received much attention so far: a first approach to update and refine topologically correct field boundaries by fusing raster images and vector map data was presented in Löcher-bach (1994).…”

Section: State Of the Art Of Geospatial Data Integrationmentioning

confidence: 99%

Integration of heterogeneous geospatial data in a federated database

Butenuth

Gösseln

Tiedge

et al. 2007

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: State Of the Art Of Geospatial Data Integrationmentioning

confidence: 99%

Integration of heterogeneous geospatial data in a federated database

Butenuth

Gösseln

Tiedge

et al. 2007

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The automatic detection of natural, small wooded and thin linear elements in rural landscapes and their differentiation when they are connected but do not underpin the same ecological processes (e.g. adjacent hedges and woods) has drawn less attention (Stach et al 2006, Thornton et al 2006, Zhang et al 2006. Because of the fine spatial resolution, largescale aerial photographs are preferred to identify such elements by applying computer-aided interpretation techniques.…”

Section: Introductionmentioning

confidence: 99%

Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach

Sheeren

Bastin

Ouin

et al. 2009

International Journal of Remote Sensing

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While small, fragmented wooded elements do not represent a large surface area in agricultural landscape, their role in the sustainability of ecological processes is recognized widely. Unfortunately, landscape ecology studies suffer from the lack of methods for automatic detection of these elements. We propose a hybrid approach using both aerial photographs and ancillary data of coarser resolution to automatically discriminate small wooded elements. First, a spectral and textural analysis is performed to identify all the planted-tree areas in the digital photograph. Secondly, an object-orientated spatial analysis using the two data sources and including a multi-resolution segmentation is applied to distinguish between large and small woods, copses, hedgerows and scattered trees. The results show the usefulness of the hybrid approach and the prospects for future ecological applications.

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“…There are many methods that can be used for image segmentation. The NDVI is one of the most widely used indices for differentiating between vegetation and non-vegetation areas in remote sensing (ZHANG et al, 2006). For the NDVI, the threshold for vegetation extraction is usually positive and near to zero, it may vary from 0.05 to 0.15.…”

Section: Vegetation Segment and Removal Of Cir Imagementioning

confidence: 99%

Study on the Feasibility of RGB Substitute Cir for Automatic Removal Vegetation Occlusion Based on Ground Close-Range Building Images

Liu

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Building 3D reconstruction based on ground remote sensing data (image, video and lidar) inevitably faces the problem that buildings are always occluded by vegetation, so how to automatically remove and repair vegetation occlusion is a very important preprocessing work for image understanding, compute vision and digital photogrammetry. In the traditional multispectral remote sensing which is achieved by aeronautics and space platforms, the Red and Near-infrared (NIR) bands, such as NDVI (Normalized Difference Vegetation Index), are useful to distinguish vegetation and clouds, amongst other targets. However, especially in the ground platform, CIR (Color Infra Red) is little utilized by compute vision and digital photogrammetry which usually only take true color RBG into account. Therefore whether CIR is necessary for vegetation segmentation or not has significance in that most of close-range cameras don't contain such NIR band. Moreover, the CIE L*a*b color space, which transform from RGB, seems not of much interest by photogrammetrists despite its powerfulness in image classification and analysis. So, CIE (L, a, b) feature and support vector machine (SVM) is suggested for vegetation segmentation to substitute for CIR. Finally, experimental results of visual effect and automation are given. The conclusion is that it's feasible to remove and segment vegetation occlusion without NIR band. This work should pave the way for texture reconstruction and repair for future 3D reconstruction.

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Automatic extraction of wind erosion obstacles by integration of GIS data, DSM and stereo images

Cited by 6 publications

References 13 publications

Integration of heterogeneous geospatial data in a federated database

Integration of heterogeneous geospatial data in a federated database

Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach

Study on the Feasibility of RGB Substitute Cir for Automatic Removal Vegetation Occlusion Based on Ground Close-Range Building Images

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