{"title":"超像素引导的高光谱图像定位保护投影和空间光谱分类","authors":"Hailong Song, Shuzhen Zhang","doi":"10.1049/ell2.13293","DOIUrl":null,"url":null,"abstract":"<p>Locality preserving projection (LPP) is a typical feature extraction method based on spectral information for hyperspectral image (HSI) classification. Recently, to improve the classification performance, the spatial information of HSI has been applied in the LPP method. However, for most of spatial–spectral-based LPP methods, they explore the spatial–spectral information within a fixed local window, which cannot be appropriate to the irregular-shape ground objects in HSI. To over this issue, an effective superpixel-guided LPP and spatial–spectral classification method are proposed, in which the spatial–adaptive structure information is fully excavated for HSI classification. Specifically, superpixel segmentation is first conducted on the HSI to generate shape-adaptive homogeneous subregions. Then, to learn more discriminative projection, the neighbourhood graph for LPP is constructed based on spatial–spectral similarity, in which pixels within the same superpixel are connected. Finally, the obtained projection feature is input a classifier to yield the initial classification result, and the edge information of ground objects captured by superpixels is utilized to optimize the initial classification result. Experiments on two real hyperspectral datasets demonstrate that the proposed superpixel-guided and spatial–spectral classification method significantly outperforms the other well-known techniques for HSI classification.</p>","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.13293","citationCount":"0","resultStr":"{\"title\":\"Superpixel-guided locality preserving projection and spatial–spectral classification for hyperspectral image\",\"authors\":\"Hailong Song, Shuzhen Zhang\",\"doi\":\"10.1049/ell2.13293\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Locality preserving projection (LPP) is a typical feature extraction method based on spectral information for hyperspectral image (HSI) classification. Recently, to improve the classification performance, the spatial information of HSI has been applied in the LPP method. However, for most of spatial–spectral-based LPP methods, they explore the spatial–spectral information within a fixed local window, which cannot be appropriate to the irregular-shape ground objects in HSI. To over this issue, an effective superpixel-guided LPP and spatial–spectral classification method are proposed, in which the spatial–adaptive structure information is fully excavated for HSI classification. Specifically, superpixel segmentation is first conducted on the HSI to generate shape-adaptive homogeneous subregions. Then, to learn more discriminative projection, the neighbourhood graph for LPP is constructed based on spatial–spectral similarity, in which pixels within the same superpixel are connected. Finally, the obtained projection feature is input a classifier to yield the initial classification result, and the edge information of ground objects captured by superpixels is utilized to optimize the initial classification result. Experiments on two real hyperspectral datasets demonstrate that the proposed superpixel-guided and spatial–spectral classification method significantly outperforms the other well-known techniques for HSI classification.</p>\",\"PeriodicalId\":11556,\"journal\":{\"name\":\"Electronics Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.13293\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronics Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/ell2.13293\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics Letters","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ell2.13293","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Superpixel-guided locality preserving projection and spatial–spectral classification for hyperspectral image
Locality preserving projection (LPP) is a typical feature extraction method based on spectral information for hyperspectral image (HSI) classification. Recently, to improve the classification performance, the spatial information of HSI has been applied in the LPP method. However, for most of spatial–spectral-based LPP methods, they explore the spatial–spectral information within a fixed local window, which cannot be appropriate to the irregular-shape ground objects in HSI. To over this issue, an effective superpixel-guided LPP and spatial–spectral classification method are proposed, in which the spatial–adaptive structure information is fully excavated for HSI classification. Specifically, superpixel segmentation is first conducted on the HSI to generate shape-adaptive homogeneous subregions. Then, to learn more discriminative projection, the neighbourhood graph for LPP is constructed based on spatial–spectral similarity, in which pixels within the same superpixel are connected. Finally, the obtained projection feature is input a classifier to yield the initial classification result, and the edge information of ground objects captured by superpixels is utilized to optimize the initial classification result. Experiments on two real hyperspectral datasets demonstrate that the proposed superpixel-guided and spatial–spectral classification method significantly outperforms the other well-known techniques for HSI classification.
期刊介绍:
Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews.
Scope
As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below.
Antennas and Propagation
Biomedical and Bioinspired Technologies, Signal Processing and Applications
Control Engineering
Electromagnetism: Theory, Materials and Devices
Electronic Circuits and Systems
Image, Video and Vision Processing and Applications
Information, Computing and Communications
Instrumentation and Measurement
Microwave Technology
Optical Communications
Photonics and Opto-Electronics
Power Electronics, Energy and Sustainability
Radar, Sonar and Navigation
Semiconductor Technology
Signal Processing
MIMO