Much research has been done on the subject of automatically determining land use and land cover in rural areas from images. However, error rates have always been too high for industrial application. The French National Mapping Agency (Institut Géographique National, IGN), is interested in automatic land cover classification for the purpose of speeding up production of highresolution topographic maps. The context at IGN is, however, slightly different from that at most other research into automatic land cover classification: First, very high spatial resolution digital images are available (50 cm per pixel), but these images have a low spectral resolution (red, green, blue, and in some cases near-infrared channels), which precludes the use of standard hyperspectral classification techniques. Second, cadastre data is available; this data gives a very rough indication of field position. Finally, IGN’s interest stems from practical goals: to reduce the amount of time spent by human photo-interpreters doing manual classifications. Therefore, IGN is not especially interested in obtaining a medium-quality land cover classification of the whole region of interest but would rather have a very high quality classification of only a portion of the terrain; the first one would have to be double-checked in full by a photo-interpreter, whereas the second one would not need further human verification, so that photo-interpreters would be able to concentrate on the remaining areas. In this Ph.D. thesis, I present a complete image analysis system which, from high-resolution 3 or 4-channel digital images (50 cm, colour and optionally near infrared), and using the cadastre database, segments the images into agriculturally-homogeneous regions (fields, forests, vines, and so on), and classifies these regions, labelling each classified region with a confidence measure which indicates the system’s confidence in each classification, and which can be used to filter out regions that are more likely to have been incorrectly classified. The process starts with a hierarchical segmentation, using a colour space, texture parameters, and shape criteria adapted to the problem of segmenting agricultural regions. This segmentation is used to register the cadastre onto the image, giving large, usually homogeneous regions. Through this registration, the system can also be used to update older classifications. Then, each of these registered cadastre regions —or, if cadastre data is not available, small regions obtained by watershed segmentation— is classified using novel probabilistic per-region classification algorithms which, unlike traditional per-pixel algorithms, do not produce saltand-pepper noise, and which also output a classification confidence measure for each classified region. These classification algorithms are supervised, and need to be trained beforehand with a ground truth defined by a photo-interpreter. As an end product we get an image segmentation into classified agriculturally-homogeneous regions, and confidence measures for each part of the segmentation, which a human photointerpreter can use to correct these results or to concentrate limited available time into the most likely errors.