ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 383-388, 2012
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/383/2012/
doi:10.5194/isprsannals-I-3-383-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
23 Jul 2012
EXTRACTION OF VINEYARDS OUT OF AERIAL ORTHO-IMAGE USING TEXTURE INFORMATION
A. Le Bris IGN, MATIS, 73 Avenue de Paris, 94160 SAINT-MANDE, FRANCE
Keywords: Classification, SIFT, Fourier, Semi-variogram, Vineyard, Land cover Abstract. A cartography of vineyards is required by many mapping agencies, both to draw topographic maps and to complete the "vineyard" layer of large scale land cover databases. In this paper, two distinct approaches are proposed and tested to achieve a (semi-)automatic detection of vineyards task out of 50cm ground resolution ortho-images. Both are object based approaches relying on image texture analysis in homogeneous land cover regions. Therefore, the first step (common to both approaches) is a segmentation of the image into homogeneous land cover regions. These regions can then be classified as vineyards or not by the next approaches. A first approach consists in a frequency analysis of the image texture in each region. A semi-variogram is first calculated from the ortho-image for each region of the segmentation. A Fourier transform (FFT) of this semi-variogram of the image is then considered. If a periodic signal with a high frequency (i.e. of which the frequency is upper than a threshold) is identified, the region is labelled as a vineyard.

The second approach is a supervised (per region) land cover classification one. It uses texture indexes calculated from ortho-images as input image information. In particular, some texture indexes derived from SIFT descriptors calculated from ortho-images have been used in the experiments, giving good results.

Conference paper (PDF, 7649 KB)


Citation: Le Bris, A.: EXTRACTION OF VINEYARDS OUT OF AERIAL ORTHO-IMAGE USING TEXTURE INFORMATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 383-388, doi:10.5194/isprsannals-I-3-383-2012, 2012.

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