Bag-of-Visual-Words Scene Classifier for Remote Sensing Image Based on Region Covariance

Scene classification is of great significance to understand the semantics and extract the information of high spatial resolution remote sensing image. The bag-of-visual-words (BOVW) model is an effective method to understand the semantic content of images. It is widely used in scene classification of remote sensing image. The existed BOVW models usually use single feature or multiple features (such as spectrum, texture, and shape) to describe visual words. However, when considering multiple low-level feature fusion strategies, most methods only combine them by simple accumulation or concatenation which can not fully learn the relationship between different features. In this letter, a visual bag-of-words scene classifier based on regional covariance (RCOVBOVW) is proposed. This method can naturally fuse multiple related features, and the covariance calculation itself has the filtering ability, which can also reduce the dimension of the features and have high efficiency. Experiments have been conducted on two public and challenging datasets (University of California (UC) Merced and Northwestern Polytechnical University (NWPU)-RESISC45), and the results show that our proposed method outperforms the most state-of-the-art methods of remote sensing image scene classification.