An Improved Bag-of-Visual-Word Based Classification Method for High-Resolution Remote Sensing Scene

Remote sensing (RS) scene classification is important for RS imagery semantic interpretation. Yet complex scenes make the task difficult. The Bag-of-Visual-Words (BoVW) method is an effective method for RS scene classification while most BoVwmethods only consider local features and ignore the import global features of the scene. This paper aims to improve the traditional scale-invariant feature transform (SIFT) based Bag-of-Visual-Words (BoVW) method which only captures local information by fusing a global feature extracted from deep convolutional neural network (DCNN) for high-resolution remote sensing (HRRS) scene classification. The proposed method enhances the representation ability for HRRS scenes by considering local and global features simultaneously and outperforms the sate-of-the-arts for obtaining accuracies of 95% on the widely used UC Merced dataset and SIRI-WHU dataset.