Scene classification using multi-scale deeply described visual words

This article presents a deep learning-based Multi-scale Bag-of-Visual Words (MBVW) representation for scene classification of high-resolution aerial imagery. Specifically, the convolutional neural network (CNN) is introduced to learn and characterize the complex local spatial patterns at different scales. Then, the learnt deep features are exploited in a novel way to generate visual words. Moreover, the MBVW representation is constructed using the statistics of the visual word co-occurrences at different scales, which are derived from a training data set.

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Recommended citation: Wenzhi Zhao, et al. (2016). “Scene classification using multi-scale deeply described visual words” International Journal of Remote Sensing. 10(7).