Soil surface roughness is a key factor for our understanding and modelling of geomorphologic processes related to exchanges of soil, water and gas. It has an impact on soil properties and tillage outcome. Soil surface roughness can be characterized both globally and locally. The interest of clod segmentation is to allow for both characterizations. Segmenting clods on a digital elevation model (DEM) of a soil surface is a complex problem because soil surfaces are complex surfaces of several level of roughness and because considering elevations results in smooth and poorly contrasted images. However, a DEM in 3 dimensions gathers more information than a profile of 1 dimension or an image of 2 dimensions.Multiresolution analysis has shown interest for roughness analysis of complex surfaces. We have used it to introduce a new approach for soil roughness analysis and to lay the foundations for clod segmentation. In this paper, we propose a complete wavelet-based approach for accurate clod contour delineation. It relies on several steps: detecting clods on the surface approximations by a supervised detection of local maxima, validating and merging the detections by shape and overlap tests, delineating the clod contours by intersecting locally the soil surface elevations with the estimated plane of the clod base and validating the contours by detecting and correcting the wrong patterns, with statistical pattern recognition. This segmentation method was evaluated in several roughness conditions, made in the laboratory, by comparison with other segmentation method. An indicator of goodness of agreement was introduced for this purpose.This wavelet-based segmentation method showed robustness to the presence of furrows and to the smoothing by rainfall and showed ability to retrieve clod diameters.