Unsupervised classification of very high remotely sensed images for grapevine rows detection

In viticulture, knowledge of vineyard vigour represents a useful tool for management. Over large areas, the grapevine vigour is mapped by remote sensing usually with vegetation indices like NDVI. To achieve good correlations between NDVI and other vine parameters the rows of a vineyard must be previously identified. This paper presents an unsupervised classification method for the identification of grapevine rows. Only the red channel of an RGB aerial image is considered as input data. The image is first masked preserving only the considered vineyard and then pre-processed with a high pass filter. The pixel populations are split in “row” and “inter-row” subset through a Ward’s modified technique. The proposed methodology is compared with standard object oriented procedure tested on six vineyards located in Tuscany using as reference manually digitalized vine rows.