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李晓龙; 张兆翔; 王蕴红; 刘庆杰; Xiaolong Li
Source Publication计算机科学与探索
Other AbstractIn 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.
KeywordAerial Image Scene Classification Bags Of Feature Deep Learning High Resolution
Document Type期刊论文
Corresponding AuthorXiaolong Li
Recommended Citation
GB/T 7714
李晓龙,张兆翔,王蕴红,等. 深度学习在航拍场景分类中的应用[J]. 计算机科学与探索,2014,8(3):305-312.
APA 李晓龙,张兆翔,王蕴红,刘庆杰,&Xiaolong Li.(2014).深度学习在航拍场景分类中的应用.计算机科学与探索,8(3),305-312.
MLA 李晓龙,et al."深度学习在航拍场景分类中的应用".计算机科学与探索 8.3(2014):305-312.
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