干旱区生态环境知识资源中心(Arid): 基于流形学习的光学遥感图像分类

Arid

	基于流形学习的光学遥感图像分类
其他题名	Optical remote sensing image classification based on manifold learning
	王云艳 1; 罗冷坤 2; 王重阳 2
来源期刊	计算机工程与科学
ISSN	1007-130X
出版年	2019
卷号	41 期号:7 页码:1212-1219
中文摘要	随着光学遥感图像技术的快速发展与广泛应用,对光学遥感图像的准确分类具有深远的研究意义。传统特征提取方式提取的高维特征中夹杂着许多冗余信息,分类过程可能导致过拟合现象,针对传统的线性降维算法不足以保持原始数据的内部结构,容易造成数据失真这一问题,提出基于流形学习的光学遥感图像分类算法。该算法首先提取出图像的SIFT特征,然后将流形学习运用于特征降维,最后结合支持向量机进行训练和识别。实验结果表明,在Satellite、NWPU和UCMerced实验数据中,冰川、建筑群和海滩分类精度得到了有效提高,达到85%左右;针对沙漠、岩石、水域等特殊环境遥感图像,分类精度提高了10%左右。总而言之,基于流形学习的分类算法对通过降维之后的数据能够保持在原高维空间中的拓扑结构,相似特征点能得到有效聚合,预防了维数灾难,减少了计算量,保证了分类精度。
英文摘要	With the rapid development and wide application of optical remote sensing image technology, accurate classification of optical remote sensing images has far-reaching research significance.The high-dimensional features extracted by traditional feature extraction methods are mixed with much redundant information,and the classification process can lead to over-fitting.The traditional linear dimension reduction algorithm cannot maintain the internal structure of the original data,and is easy to cause data distortion.We propose an optical remote sensing image classification algorithm based on manifold learning.Firstly,the SIFT features of the image are extracted,and the manifold learning is applied to feature dimension reduction.Finally,the support vector machine is used for training and recognition. Experimental results show that the classification accuracy of glaciers,buildings and beaches is effectively improved on the experimental data of Satellite,NWPU and UC Merced,reaching about 85%.For remote sensing images of desert,rock and water,the classification accuracy is improved by about 10%.In summary,the data based on manifold learning can maintain the topological structure in the original highdimensional space through the dimension reduction algorithm.Similar feature points can effectively aggregate, which prevents the"dimensional disaster",reduces the calculation amount and guarantees classification accuracy.
中文关键词	流形学习 ; 遥感图像 ; 图像分类 ; 支持向量机
英文关键词	manifold learning remote sensing image image classification support vector machine
语种	中文
国家	中国
收录类别	CSCD
WOS类目	COMPUTER SCIENCE INTERDISCIPLINARY APPLICATIONS
WOS研究方向	Computer Science
CSCD记录号	CSCD:6534836
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/239290
作者单位	1.湖北工业大学电气与电子工程学院;;太阳能高效利用及储能运行控制湖北省重点实验室, ;;太阳能高效利用及储能运行控制湖北省重点实验室, 武汉;;武汉, 湖北;;湖北 430068;;430068, 中国; 2.湖北工业大学电气与电子工程学院, 武汉, 湖北 430068, 中国
推荐引用方式 GB/T 7714	王云艳,罗冷坤,王重阳. 基于流形学习的光学遥感图像分类[J],2019,41(7):1212-1219.
APA	王云艳,罗冷坤,&王重阳.(2019).基于流形学习的光学遥感图像分类.计算机工程与科学,41(7),1212-1219.
MLA	王云艳,et al."基于流形学习的光学遥感图像分类".计算机工程与科学 41.7(2019):1212-1219.