Mining the spatial distribution of visual words for scene classification

In the past decades, tremendous investigations have been made to classify high-spatial-resolution remote sensing (HSR-RS) images at scene level. Among them, Bag-of-Visual-Words (BoVW) model has been widely used thanks to its robustness and efficiency. However, such a representation leaves out the spatial information of the image which is very important for distinguishing various scenes. In this paper, we aim to mine the spatial distribution of the visual words in the BoVW methods so as to incorporate the spatial information of the HSR-RS image and improve the classification accuracy. More precisely, we start from a BoVW representation of each scene image, and then compute local spatial features from this representation, i.e. the encoded image with BoVW model
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. The marginal distributions of these local spatial features are finally used to describe HSR-RS scene images. In particular, the local spatial features we used in this paper include the local binary pattern (LBP) and re-learned BoVW dictionaries. The method has been evaluated on a large-scale HSR-RS image dataset, i.e. WHU20, that consists of 5000 HSR-RS images with 20 semantic classes for scene classification. The experimental results show that our method can improve the classification accuracy a lot compared with the standard BoVW method.