Title: Automatic Detection and Segmentation of Orchards Using Very High-Resolution Imagery
Authors: AKSOY SelimYALNIZ Ismet ZekiTASDEMIR KADIM
Citation: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING vol. 50 no. 8 p. 3117-3131
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Year: 2012
JRC Publication N°: JRC66166
ISSN: 0196-2892
URI: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6144003
http://publications.jrc.ec.europa.eu/repository/handle/JRC66166
DOI: 10.1109/TGRS.2011.2180912
Type: Articles in Journals
Abstract: Spectral information alone is often not sufficient to distinguish certain terrain classes such as permanent crops like orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive spatial structures that can be observable in detail in very high spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards. The detection step uses a texture model that is based on the idea that textures are made up of primitives (trees) appearing in a near-regular repetitive arrangement (planting patterns). The algorithm starts with the enhancement of potential tree locations by using multigranularity isotropic filters. Next, the structure in the locations of the local extrema in the filter responses is analyzed by converting the image data into 1D signals by using projection profiles within oriented sliding windows. Then, the regularity of the planting patterns is quantified in terms of the periodicity of the profiles at multiple orientations. The result is a regularity score at each pixel for each granularity and orientation. Finally, the segmentation step iteratively merges neighboring pixels and regions belonging to similar planting patterns according to the similarities of their regularity scores, and obtains the boundaries of individual orchards along with estimates of their granularities and orientations. Extensive experiments using Ikonos and QuickBird imagery as well as images taken from Google Earth show that the proposed algorithm provides good localization of the target objects even when no sharp boundaries exist in the image data.
JRC Institute:Institute for Environment and Sustainability

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