A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from traditional CBIR is the widespread occurrences of weak textures. The task of representing the weak textures becomes even more challenging especially if image properties like scale, illumination or the viewing geometry are not known. In this work, we have proposed the use of a new feature ‘texton histogram' to capture the weak-textured nature of remote sensing images. Combined with an automatic classifier, our texton histograms are robust to variations in scale, orientation and illumination conditions as illustrated experimentally. The classification accuracy is further improved using additional image driven features obtained by the application of a feature selection procedure.