@inproceedings{5b9f0525442f44a4a2bf98cf32b38964,
title = "Scene recognition for complicated UAV images based on land surface classification of superpixel",
abstract = "Scene recognition has been playing a central role in image processing. In this filed, researchers usually construct a semantic feature model of images. However, the feature model can't intuitively and effectively represent the image for complicated images of unmanned aerial vehicle (UAV). To solve this problem, we propose the idea of divide and rule to decompose scene recognition into superpixel segmentation and surface classification. In this process, an adaptive superpixel segmentation method based on UAV metadata is proposed to segment image into superpixel regions with identical surface. Then, multi-features combination and sparse coding semantic feature are used to respectively describe the image complexity and the content of superpixel regions. It's shown that the accuracy of scene recognition reaches 95\% in samples of complicated UAV images testifying that our algorithm can recognize scenes in complicated UAV images by rule and line.",
keywords = "Scene recognition, Sparse coding, Superpixel, Surface classification, UAV image",
author = "Yang Shi and Wenrui Ding and Hongguang Li and Shuo Liu",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 2017 International Conference on Artificial Intelligence, Automation and Control Technologies, AIACT 2017 ; Conference date: 07-04-2017 Through 09-04-2017",
year = "2017",
month = apr,
day = "7",
doi = "10.1145/3080845.3080847",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
editor = "Dan Zhang and Songyi Dian",
booktitle = "Proceedings - 2017 International Conference on Artificial Intelligence, Automation and Control Technologies, AIACT 2017",
address = "美国",
}