  Scene classification is a key problem in the interpretation of
high-resolution remote sensing imagery. Many state-of-the-art methods, e.g.
bag-of-visual-words model and its variants, the topic models as well as deep
learning-based approaches, share similar procedures: patch sampling, feature
description/learning and classification. Patch sampling is the first and a key
procedure which has a great influence on the results. In the literature, many
different sampling strategies have been used, {e.g. dense sampling, random
sampling, keypoint-based sampling and saliency-based sampling, etc. However, it
is still not clear which sampling strategy is suitable for the scene
classification of high-resolution remote sensing images. In this paper, we
comparatively study the effects of different sampling strategies under the
scenario of scene classification of high-resolution remote sensing images. We
divide the existing sampling methods into two types: dense sampling and sparse
sampling, the later of which includes random sampling, keypoint-based sampling
and various saliency-based sampling proposed recently. In order to compare
their performances, we rely on a standard bag-of-visual-words model to
construct our testing scheme, owing to their simplicity, robustness and
efficiency. The experimental results on two commonly used datasets show that
dense sampling has the best performance among all the strategies but with high
spatial and computational complexity, random sampling gives better or
comparable results than other sparse sampling methods, like the sophisticated
multi-scale key-point operators and the saliency-based methods which are
intensively studied and commonly used recently.
