关键词: 航拍, 场景分类, 视觉词袋, 深度学习, 高分辨率

Abstract: In recent decades, aerial image/video processing has been widely studied for urban planning, coastal monitoring and military tasks. Therefore, understanding the contents contained in aerial images and studying the scene classification of aerial videos are very important. However, currently most popular scene classification algorithms are mainly for natural scenes, rarely for high resolution aerial scene classification. This paper proposes a hierarchical scene classification model for aerial videos/images. Firstly, the scale-invariant feature transform (SIFT) vector is extracted as the patch feature. Then, on the basis of utilizing bag of words, the deep belief network (DBN) initialized by restricted Boltzmann machine (RBM) is used to obtain the latent variables which describe the relationship between low-level region features and high-level global features. The DBN also plays as a classifier. The proposed method achieves promising performance compared with the state of art scene classification methods.

Key words: aerial image, scene classification, bags of feature, deep learning, high resolution