A GPU implementation of a structural-similarity-based aerial-image classification

Abstract

There is an increasing need for fast and efficient algorithms for the automatic analysis of remote-sensing images. In this paper we address the implementation of the semantic classification of aerial images with general-purpose graphics-processing units (GPGPUs). We propose the calculation of a local Gabor-based structural texture descriptor and a structural texture similarity metric combined with a nearest-neighbor classifier and image-to-class similarity on CUDA supported graphics-processing units. We first present the algorithm and then describe the GPU implementation and optimization with the CUDA programming model. We then evaluate the results of the algorithm on a dataset of aerial images and present the execution times for the sequential and parallel implementations of the whole algorithm as well as measurements only for the selected steps of the algorithm. We show that the algorithms for the image classification can be effectively implemented on the GPUs. In our case, the presented algorithm is around 39 times faster on the Tesla C1060 unit than on the Core i5 650 CPU, while keeping the same success rate of classification.