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Remote Sensing Technology and Application  2021, Vol. 36 Issue (3): 627-637    DOI: 10.11873/j.issn.1004-0323.2021.3.0627
    
Panchromatic / Multispectral Image Fusion Method and Application Analysis for Coastal Wetland: A Case Study of Hangzhou Bay(1999-2018)
Jiajia Li1(),Jinfang Shu2,Zenghuan Qiu3,Randi Fu1(),Wei Jin1
1.College of Information Science and Engineering,Ningbo University,Ningbo 315000,China
2.Ningbo Institute of Surveying,Mapping,and Remote Sensing,Ningbo 315000,China
3.College of Communication and Information Engineering,Shanghai University,Shanghai 200000,China
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Abstract  

Image fusion plays an important role in the remote sensing monitoring of coastal wetlands. However, the coastal wetlands are mainly composed of water, plants and so on, which leads to spectral distortion, and poor robustness of existing fusion methods. Therefore, in order to meet the demand of robust panchromatic / multispectral image fusion method for coastal wetland remote sensing monitoring, we propose a panchromatic / multispectral image fusion method (SSQI_PANSHARP) based on spatial-spectral quality evaluation, to integrate the advantage of different fusion methods. In this study, Hangzhou Bay coastal wetland was taken as the research area. Based on the 20 year image data of Landsat-7 and Landsat-8 from 1999 to 2018, the proposed method was fully verified from qualitative and quantitative aspects, as well as NDVI and NDWI. On this basis, the data of three periods in the same quarter are extracted evenly, while the land cover types of Hangzhou Bay Coastal Wetland in recent 20 years were monitored and analyzed. The results show that the SSQI_PANSHARP method has better spectral fidelity, and spatial structure information enhancement effect in the coastal wetland area, and strong robustness, which is suitable for coastal wetland monitoring needs. In addition, long-term dynamic monitoring showed that the proportion of construction land increased after 2010, the pond paddy field increased significantly, and the farmland green space decreased.

Key words:  Image fusion      Panchromatic/Multispectral      Coastal wetland      Long-term dynamic monitoring     
Received:  05 August 2020      Published:  22 July 2021
ZTFLH:  TP75  
Corresponding Authors:  Randi Fu     E-mail:  15839716936@163.com;furandi@nbu.edu.cn
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Jiajia Li
Jinfang Shu
Zenghuan Qiu
Randi Fu
Wei Jin

Cite this article: 

Jiajia Li,Jinfang Shu,Zenghuan Qiu,Randi Fu,Wei Jin. Panchromatic / Multispectral Image Fusion Method and Application Analysis for Coastal Wetland: A Case Study of Hangzhou Bay(1999-2018). Remote Sensing Technology and Application, 2021, 36(3): 627-637.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2021.3.0627     OR     http://www.rsta.ac.cn/EN/Y2021/V36/I3/627

Fig.1  Flow chart of fusion algorithm based on space-spectrum quality evaluation
输入:低空间高光谱分辨率的多光谱图像(MS),高空间低光谱分辨率的全色图像(PAN
输出:高空间高光谱分辨率的目标融合图像(FUSION)。
步骤1:通过典型成分替换类融合方法和多分辨率分析类融合方法得到初步融合结果,本文选取3种成分替换类融合算法,包括IHS、GS、PCA,选取2种多分辨率分析类融合算法,包括MTF_GLP、AWLP。
步骤2:设计空—谱联合质量评价指标SSQI,用以评价初步融合结果各像元的空谱质量。

(1)通过公式(1)计算初步融合影像的光谱评价指标Se;

(2)通过公式(2)、(3)计算初步融合影像的空间评价指标Sa;

(3)通过公式(4)计算空—谱联合质量评价指标SSQI。
步骤3:基于SSQI进行初步融合结果的二次融合,得到最终目标融合图像(FUSION)。
Table 1  Steps of the proposed algorithm
传感器类型获取时间分辨率图像大小
ETM1999年11月 2000年5月 2001年11月

多光谱30 m

全色15 m

1 000×1 000

2 000×2 000

2002年8月 2003年5月 2005年9月
2006年9月 2007年5月 2008年6月
2009年5月 2010年5月 2011年9月
2012年11月
OLI2013年5月 2014年5月 2015年7月

多光谱30 m

全色15 m

1 000×1 000

2 000×2 000

2016年2月 2017年9月 2018年1月
Table 2  Data set list
Fig.2  Fusion images in 2018
Fig.3  Typical topographic detail maps
Fig.4  Quantitative evaluation of 20 year fused images
Fig.5  Line chart of NDVI / NDWI index correlation coefficient for 20 year fused images
Fig.6  Comparison of Spectral Curves before and after fusion (2018)
Fig.7  Comparison of phase III images before and after fusion
Fig.8  Classification results of phase III images
池塘水田海水水域滩涂沼泽农田绿地建筑用地错分误差/%使用者精度/%
池塘水田6 60700541 22635919.8880.12
海水水域061 1081060000.1799.83
滩涂029266 9201 011001.9198.09
沼泽3304 73520 938038419.7580.25
农田绿地63200613 4591 24112.2587.75
建筑用地9019427633519 1704.0795.93
漏分误差/%9.260.486.996.0410.399.38
生产者精度/%90.7499.5293.0193.9689.6190.62
总体分类精度 =(188202/199095)=94.53% Kappa系数 =0.93
Table 3  Confusion matrix of image data classification result (2017 as an example)
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