A comparative study of sampling analysis in scene classification of high-resolution remote sensing imagery

Scene classification is a key problem in the interpretation of high-resolution remote sensing imagery. The state-of-the-art methods, e.g. bag-of-visual-words model and its various extensions as well as the topic models, share similar procedures: patch sampling, feature description/learning and classification. Patch sampling is the first and the key procedure which has a great influence on the results. In this paper, we focus on the effects of different sampling strategies used in the literature sa as to find a suitable sampling strategy for the scene classification of high-resolution remote sensing images. We divide the existing sampling methods into two types: random sampling and saliency-based sampling, and embed them in the bag-of-visual-words framework for comparison owing to its simplicity, robustness and efficiency. Moreover, we compare it using another framework - Fisher kernel, to validate our conclusions. The experimental results on two commonly used datasets using two different frameworks both show that random sampling can give better or comparable results than other sampling methods.