Gilbert Grenier

Delineation of vine parcels by segmentation of high resolution remote sensed images

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.

Texture based image retrieval and classification of very high resolution maritime pine forest images

Textural analysis can bring valuable information in the classification or the segmentation process of land covers displaying regular patterns in very high resolution remotely sensed images. In this study, we investigate how features extracted by multivariate modeling of the local spatial dependence in the wavelet domain can efficiently capture the textural content of maritime pine forest images in comparison with a commonly used texture analysis approach, the GLCM. To evaluate the performances of the tested methods, we used a content based image retrieval framework and created a database of image patches representing different development stages of the forest stands. Results show that multivariate models display higher retrieval rates than GLCM-based methods with yet a higher sensitivity to the dominant orientation in anisotropic textures. These observations open up new perspectives in the use of multivariate modeling for textural features extraction in very high resolution image classification.

Abundance weighting for improved vegetation mapping in row crops: application to vineyard vigour monitoring

We present a complete framework for vigour mapping in row crops by multispectral remote sensing. The main contribution consists of taking into account vegetation abundance in the computation of vigour indexes. Though developed in a viticulture context, the proposed algorithm is generic enough to be adapted to any row crop, especially in horticulture. The algorithm takes advantage of both spectral and spatial features extracted from image data. Spectral information is used at pixel level by an independent component analysis (ICA) based algorithm to process vegetation abundance maps. As for spatial information, deformable models are used to fit a network of rectangles to individual plants. Both spectral information and spatial information are then combined to compute abundance-weighted vigour indexes that are assigned to specific plants. Resulting measurements are then used for within-block vigour mapping. A validation procedure is carried out on experimental vine plots. It is shown that the use of vegetation abundance by itself or as a weight in the computation of vegetation indexes improves the accuracy of vigour assessment in row crops.