Fast binary coding for satellite image scene classification

Feature extraction is at the core of satellite scene classification task. In this paper, we propose a fast binary coding (FBC) method to effectively generate the global discriminative feature representation of image scenes. Equipped with unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly sampled image patches, and then we obtain feature maps by convolving image scene with the learned filter bank. After binarizing the feature maps, a simple skillful conversion of binary-valued feature map to integer-valued feature map is performed. The final statistical histograms, which are considered as the global feature representations of scenes, are computed on the integer-valued feature map similar to the conventional BOW model. Experiments on two datasets demonstrate that the proposed FBC achieve satisfying classification performance as well as has much faster computational speed compared with traditional scene classification methods.