Fusion of Global and Local Descriptors for Remote Sensing Image Classification

Very high resolution remote sensing images offer increased amount of details available for image interpretation. However, despite enhanced resolution, these details result in spectral inhomogeneities, making automated image classification more difficult. In this letter, we propose to combine texture and local image features to address this problem. We first address the enhanced Gabor texture descriptor which is a global descriptor based on cross correlations between subbands and show that it achieves very good results in classification of aerial images showing a single thematic class. Next, the performances obtained on individual land cover/land use classes using our global texture descriptor and local scale-invariant feature transform descriptor are compared. We identify classes of images best suited for each descriptor and argue that these descriptors encode complementary information. Finally, a hierarchical approach for the fusion of global and local descriptors is proposed and evaluated over a number of classifiers. The proposed descriptor fusion approach exhibits significantly improved classification results, reaching the accuracy of around 90%.