基于不规则尺度区域光谱信息的高光谱图像亚像元定位
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1.漳州测绘学院 自然资源部东南沿海海洋信息智能感知与应用重点实验室,福建 漳州363000;2.滁州学院 实景地理环境安徽省重点实验室,安徽 滁州239000;3.南京航空航天大学 电子信息工程学院,江苏 南京210016;4.南京邮电大学 管理学院,江苏 南京210003;5.河北省气象科学研究所 河北省气象与生态环境重点实验室,河北 石家庄050021;6.长安大学 西安市国土空间信息重点实验室,陕西 西安710064

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TP751

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Sub-pixel mapping based on spectral information of irregular scale areas for hyperspectral images
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1.Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, Ministry of Natural Resources, Zhangzhou Institute of Surveying and Mapping, Zhangzhou 363000, China;2.Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou University, Chuzhou 239000, China;3.College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;4.School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;5.Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Meteorological Institute of Hebei, Shijiazhuang 050021, China;6.Xi’an Key Laboratory of Territorial Spatial Information, Chang'an University, Xi’an 710064, China

Fund Project:

Supported by the Foundation of Anhui Province Key Laboratory of Physical Geographic Environment (2022PGE010); The Fundamental Research Funds for the Central Universities, CHD (300102353508); the Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, MNR (22101); National Natural Science Foundation of China (61801211); Natural Science Foundation of Jiangsu Province (BK20221478); Hong Kong Scholars Program (XJ2022043); S&T Program of Hebei (21567624H); Open Project Program of Key Laboratory of Meteorology and Ecological Environment of Hebei Province (Z202102YH)

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    摘要:

    亚像元定位技术可以分析混合像元,并实现从丰度图像到亚像元级精细土地覆盖定位图像的转换。然而,传统的亚像元定位方法所使用的光谱信息通常在指定的矩形局部窗口中构造,并且很少使用所有波段的光谱信息,影响了亚像元定位的性能。为了解决这一问题,本文提出了一种基于不规则尺度区域光谱信息的高光谱图像亚像元定位方法 (SIISA)。在三幅遥感图像上的实验结果表明,所提出的SIISA优于现有的亚像元定位方法。

    Abstract:

    Sub-pixel mapping technology can analyze mixed pixels and realize the transformation from fractional images to fine a land-cover mapping image at the sub-pixel level. However, the spectral information used by the traditional sub-pixel mapping methods is usually constructed in a specified rectangular local window, and the spectral information of all bands is rarely used, affecting the performance of sub-pixel mapping. To solve this issue, sub-pixel mapping based on spectral information of irregular scale areas (SIISA) for hyperspectral images is proposed in this paper. The experimental results on three remote sensing images show the proposed SIISA outperforms the existing sub-pixel mapping methods.

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王鹏,陈永康,张弓,王弘颖,赵春雷,韩玲.基于不规则尺度区域光谱信息的高光谱图像亚像元定位[J].红外与毫米波学报,2023,42(4):538~545]. WANG Peng, CHEN Yong-Kang, ZHANG Gong, WANG Hong-Ying, ZHAO Chun-Lei, HAN Ling. Sub-pixel mapping based on spectral information of irregular scale areas for hyperspectral images[J]. J. Infrared Millim. Waves,2023,42(4):538~545.]

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  • 收稿日期:2022-07-12
  • 最后修改日期:2023-06-02
  • 录用日期:2023-02-28
  • 在线发布日期: 2023-06-02
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