J.-P. Da Costa

Hyperspectral image analysis for precision viticulture

We analyze the capabilities of CASI data for the discrimination of vine varieties in hyperspectral images. To analyze the discrimination capabilities of the CASI data, principal components analysis and linear discriminant analysis methods are used. We assess the performance of various classification techniques: Multi-layer perceptrons, radial basis function neural networks, and support vector machines. We also discuss the trade-off between spatial and spectral resolutions in the framework of precision viticulture.

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.

Vine variety discrimination with airborne imaging spectroscopy

We aim at the discrimination of varieties within a single plant species (Vitis vinifera) by means of airborne hyperspectral imagery collected using a CASI-2 sensor and supervised classification, both under constant and varying within-scene illumination conditions. Varying illumination due to atmospheric conditions (such as clouds) and shadows cause different pixels belonging to the same class to present different spectral vectors, increasing the within class variability and hindering classification. This is specially serious in precision applications such as variety discrimination in precision agriculture, which depends on subtle spectral differences. In this study, we use machine learning techniques for supervised classification, and we also analyze the variability within and among plots and within and among sites, in order to address the generalizability of the results.

Assessment of Texture Stationarity Using the Asymptotic Behavior of the Empirical Mean and Variance

Given textured images considered as realizations of 2-D stochastic processes, a framework is proposed to evaluate the stationarity of their mean and variance. Existing strategies focus on the asymptotic behavior of the empirical mean and variance (respectively EM and EV), known for some types of nondeterministic processes. In this paper, the theoretical asymptotic behaviors of the EM and EV are studied for large classes of second-order stationary ergodic processes, in the sense of the Wold decomposition scheme, including harmonic and evanescent processes. Minimal rates of convergence for the EM and the EV are derived for these processes; they are used as criteria for assessing the stationarity of textures. The experimental estimation of the rate of convergence is achieved using a nonparametric block sub-sampling method. Our framework is evaluated on synthetic processes with stationary or nonstationary mean and variance and on real textures. It is shown that anomalies in the asymptotic behavior of the empirical estimators allow detecting nonstationarities of the mean and variance of the processes in an objective way.