No abstract

Field delineation is an essential preliminary step for the design of management maps for grape production. In this paper, we propose a new algorithm for the segmentation of vine fields based on high-resolution remote sensed images. This algorithm takes into account the textural properties of vine images. It leads to the computation of a textural attribute on which a simple thresholding operation allows to discriminate between vine field and non-vine field pixels. The feasibility of the automatic delineation is illustrated on a range of vineyard images with various inter-row distances, grass covers, perspective distortions and side perturbations. In most cases it produces precise delineation of field borders while the parcel under consideration remains separate from the rest of the image.

In the last decades, orientation estimation has often been investigated for instance in the domain of still image analysis for feature extraction [5] or in the context of video stream processing for motion analysis [10] [20] [27]. Applications of orientation estimation vary, for example, from the enhancement of ancient engravings to the analysis of fingerprint images or seismic data [8]. Orientation relates to the direction of the apparent structures in the observed area. At a given location in an image, orientation depends on the size of the observation window, which corresponds to the scale of analysis. Statistical techniques applied to orientation vectors (as for instance PCA [8], Rao's algorithm [5][22] or the tensor-based framework proposed by Knutsson [14]) allow to compute orientations at a large scale from orientations at a local scale. Given the capabilities of such techniques, we focus specifically on local orientation estimation. Local orientation estimation is often based on the computation of local derivatives [6][7][8][17] [22], assuming that orientation is orthogonal to the gradient vector. Nevertheless, gradient based approaches rely on the unicity of orientation at a given point and are not suitable if several orientations occur at a given location. As an illustration, the texture in Fig. 1.a shows two components with different orientations, one at 20° the other one at 60°. The spatial period of both components is 10 pixels. A structure tensor with a computing support size of 55 pixels estimates the main orientation of the texture at approximately 32° (Fig. 1.b). Indeed, this Fig.1