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A Superpixel Spatial Intuitionistic Fuzzy C-Means Clustering Algorithm for Unsupervised Classification of High Spatial Resolution Remote Sensing Images

文献类型：期刊论文

作者Ji, Xinran1; Huang, Liang1,2; Tang, Bo-Hui1,3; Chen, Guokun1; Cheng, Feifei1
刊名REMOTE SENSING
出版日期2022-07-01
卷号14期号:14页码:22
关键词intuitionistic fuzzy C-means clustering superpixel classification high spatial resolution remote sensing image
DOI10.3390/rs14143490
通讯作者Huang, Liang(kmhuangliang@kust.edu.cn)
英文摘要This paper proposes a superpixel spatial intuitionistic fuzzy C-means (SSIFCM) clustering algorithm to address the problems of misclassification, salt and pepper noise, and classification uncertainty arising in the pixel-level unsupervised classification of high spatial resolution remote sensing (HSRRS) images. To reduce information redundancy and ensure noise immunity and image detail preservation, we first use a superpixel segmentation to obtain the local spatial information of the HSRRS image. Secondly, based on the bias-corrected fuzzy C-means (BCFCM) clustering algorithm, the superpixel spatial intuitionistic fuzzy membership matrix is constructed by counting an intuitionistic fuzzy set and spatial function. Finally, to minimize the classification uncertainty, the local relation between adjacent superpixels is used to obtain the classification results according to the spectral features of superpixels. Four HSRRS images of different scenes in the aerial image dataset (AID) are selected to analyze the classification performance, and fifteen main existing unsupervised classification algorithms are used to make inter-comparisons with the proposed SSIFCM algorithm. The results show that the overall accuracy and Kappa coefficients obtained by the proposed SSIFCM algorithm are the best within the inter-comparison of fifteen algorithms, which indicates that the SSIFCM algorithm can effectively improve the classification accuracy of HSRRS image.
WOS关键词SCENE CLASSIFICATION ; SEGMENTATION
资助项目National Natural Science Foundation of China[41961039] ; Yunnan Fundamental Research Projects[202201AT070164] ; Yunnan Fundamental Research Projects[202101AT070102]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000833299500001
出版者MDPI
资助机构National Natural Science Foundation of China ; Yunnan Fundamental Research Projects
源URL[http://ir.igsnrr.ac.cn/handle/311030/181162]  
专题中国科学院地理科学与资源研究所
通讯作者Huang, Liang
作者单位1.Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Yunnan, Peoples R China
2.Surveying & Mapping Geoinformat Technol Res Ctr P, Kunming 650093, Yunnan, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Ji, Xinran,Huang, Liang,Tang, Bo-Hui,et al. A Superpixel Spatial Intuitionistic Fuzzy C-Means Clustering Algorithm for Unsupervised Classification of High Spatial Resolution Remote Sensing Images[J]. REMOTE SENSING,2022,14(14):22.
APA Ji, Xinran,Huang, Liang,Tang, Bo-Hui,Chen, Guokun,&Cheng, Feifei.(2022).A Superpixel Spatial Intuitionistic Fuzzy C-Means Clustering Algorithm for Unsupervised Classification of High Spatial Resolution Remote Sensing Images.REMOTE SENSING,14(14),22.
MLA Ji, Xinran,et al."A Superpixel Spatial Intuitionistic Fuzzy C-Means Clustering Algorithm for Unsupervised Classification of High Spatial Resolution Remote Sensing Images".REMOTE SENSING 14.14(2022):22.

入库方式： OAI收割

来源：地理科学与资源研究所

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