Methods for extracting features and classifying textures in high resolution colour images are presented. The proposed features are directional texture features obtained from the convolution of the Walsh-Hadamard transform with different orientations of texture patches from high resolution images, as well as simple chromatic features that correspond to hue and saturation in the HLS colour space. We compare the performance of these new features against Gabor transform features combined with HLS and Lab colour space features. Multiple classifiers are employed to combine both textural and chromatic features for better classification performance. We demonstrate a considerable reduction in computational costs, whilst maintaining close accuracy.