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The Texture Fragmentation and Reconstruction (TFR) algorithm, recently proposed for the segmentation of textured images, has been applied with promising results to high-resolution remote-sensing images. The algorithm provides a sequence of nested segmentation maps which allow the analysis at various scales of observation. However, the performance which is very good at large scales, with complex semantic areas retrieved with remarkable accuracy, becomes less satisfactory at finer scales. In this paper we propose to use the TFR in a recursive fashion, segmenting the image in just two regions, initially, with each region further segmented only if relevant subregions emerge. The recursive TFR allows one to better adapt to local statistics and to extract significant textures also at finer scales. Early experimental results validate the effectiveness of the new algorithm.