In this paper, we present a new image processing algorithm to analyse fine sediment deposits on gravel bars. The method is based on pictures taken from the river bank with an average resolution of a few centimetres per pixel. The proposed image processing algorithm consists of two steps. In the first step, the raw image is orthorectified with a 5 cm pixel size. In second step, we used an original combination of two well-known image techniques to classify pixel. First, a high-pass filter is applied in the Fourier domain to correct grey-value potential bias, i.e. to merge wet and dry deposits. Then, an unsupervised classification process based on K-means clustering is conducted to partition image pixels into K classes. The area of fine sediment deposits is calculated by summing the area of each class multiplied by weighting parameters (surface area percentage of fine deposits for each class). The value of K and the weighting parameters are calibrated using local close range photos of the bar surface representing the spatial variation of surface fine sediment distribution. The algorithm is then validated with field measurement using the finest class (d < 4 mm) of Wolman pebble count method. The impact of image resolution is investigated: the algorithm was found suitable for pixel size between 1 and 10 cm. The robustness of the present algorithm to images taken at different times and with different illuminations is studied by comparing it to two methods from the literature. The new method yields the most robust results with an average standard deviation lower than 5%. It yields larger surface of fine sediment since it includes all possible patches around coarse particles. Such results imply that the algorithm can be applied on a long image series to help us understand the temporal dynamics of fine sediment deposits on the bar surface.