| 基于动态阈值哈希的大规模遥感影像快速内容检索方法 |
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| 引用本文： | 强永刚,肖志峰,陈欢欢,闫丽阳.基于动态阈值哈希的大规模遥感影像快速内容检索方法[J].测绘通报,2019,0(8):34-38,53. |
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| 作者姓名： | 强永刚 肖志峰 陈欢欢 闫丽阳 |
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| 作者单位： | 中国科学技术大学计算机科学与技术学院,安徽合肥,230027;武汉大学测绘遥感信息工程国家重点实验室,湖北武汉,430079 |
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| 基金项目： | 高分专项青年创新基金（GFZX04061502） |
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| 摘 要： | 随着我国遥感对地观测技术的快速发展，接收和存档的遥感影像数据量呈指数级增长，传统的检索方法难以在超大的遥感影像数据量上进行快速内容检索，造成遥感影像检索技术缺乏突破性进展，使得我国遥感影像利用率和利用效率受到限制。本文提出了一种创新的哈希索引方法，该方法根据特征向量的空间分布情况动态生成向量的哈希编码，可对高维的遥感影像特征向量进行低维编码，大大降低了检索计算量，可显著提高大规模遥感影像库内容检索的准确率和效率。在天地图数据集的检索试验表明本文提出方法在准确度和检索效率上均有显著提升，有较大的应用潜力。
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| 关 键 词： | 遥感影像检索 哈希算法 特征索引 降维 |
| 收稿时间： | 2018-11-01 |
| 修稿时间： | 2019-02-15 |
Content retrieval of large-scale remote sensing images based on dynamic threshold hashing |
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| QIANG Yonggang,XIAO Zhifeng,CHEN Huanhuan,YAN Liyang.Content retrieval of large-scale remote sensing images based on dynamic threshold hashing[J].Bulletin of Surveying and Mapping,2019,0(8):34-38,53. |
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| Authors: | QIANG Yonggang XIAO Zhifeng CHEN Huanhuan YAN Liyang |
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| Institution: | 1. College of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | With the rapid development of remote sensing earth observation technology in China, the amount of remote sensing image data received and archived has increased exponentially. The traditional retrieval methods are difficult to retrieve the large amount of remote sensing image data quickly, resulting in the lack of breakthrough in remote sensing image retrieval technology, the utilization ratio and utilization efficiency of remote sensing images in China are very limited. In this paper, an innovative hash index method is proposed, which generates the hash codes dynamically according to the spatial distribution of the feature vectors. This method can encode the feature vectors of high-dimensional remote sensing images in low dimensions, greatly reduces the amount of retrieval computation and significantly improves the retrieval accuracy and efficiency of large-scale remote sensing image database. The retrieval experiments on the sky map data set show that the proposed method has a significant improvement in accuracy and retrieval efficiency, and has a great application potential. |
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| Keywords: | remote sensing image retrieval hash algorithm feature index dimensionality reduction |
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