<italic style="font-style: italic">Ulva prolifera</italic> subpixel mapping with multiple-feature decision fusion

Jianhua WAN; Xianci WAN; Lie SUN; Mingming XU; Hui SHENG; Shanwei LIU; Bin ZOU; Qimao WANG

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Ulva prolifera subpixel mapping with multiple-feature decision fusion

Physics | Updated：2024-10-12

- Ulva prolifera subpixel mapping with multiple-feature decision fusion
- Journal of Oceanology and Limnology Vol. 41, Issue 3, Pages: 865-880(2023)
- Affiliations：
  
  1.College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
  2.Qingdao Ecological and Environment Monitoring Center of Shandong Province, Qingdao 266003, China
  3.National Satellite Ocean Application Service, Beijing 100081, China
  4.Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources (MNR), Beijing 100081, China
- Author bio：
  
  xumingming900405@126.com
  sheng@upc.edu.cn
- Funds：
- DOI：
  CLC：
- Received：10 October 2021，
  
  Published：01 May 2023
- Accepted：
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WAN Jianhua,WAN Xianci,SUN Lie,et al.,Ulva prolifera subpixel mapping with multiple-feature decision fusion[J].Journal of Oceanology and Limnology,2023,41(03):865-880.
DOI：

WAN Jianhua,WAN Xianci,SUN Lie,et al.,Ulva prolifera subpixel mapping with multiple-feature decision fusion[J].Journal of Oceanology and Limnology,2023,41(03):865-880. DOI：

摘要

Abstract

The unavoidable nature of

Ulva prolifera

mixed pixel in low-resolution remote sensing images would result in rough boundary of

prolifera

patches

omission of tiny patches

and overestimation of coverage area. The decomposition of

prolifera

mixed pixel addresses the issue of coverage area overestimation

and the remaining problems can be alleviated by subpixel mapping (SPM). Due to the drift and dissipation of

prolifera

a suitable SPM method is the single image-based unsupervised method. However

the method has difficulties in detail reconstruction

insufficient learning of spectral information

and SPM error introduced by abundance deviation. Therefore

we proposed a multiple-feature decision fusion SPM (MFDFSPM) method. It in

volves three branches to obtain the spatial

abundance

and spectral features of

prolifera

while considers multi-feature information using the fusion strategy. Experiments on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical

prolifera

SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation

which produced subpixel map with more detailed spatial information and less noise.

关键词

Keywords

references

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Ulva prolifera subpixel mapping with multiple-feature decision fusion

DOI：

摘要

Abstract

关键词

Keywords

references