Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea
Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea
复制标题
使用反向传播人工神经网络模型进行数据稀缺区域的数据融合:以南海为例
DOI:
10.1007/s11707-017-0652-1
复制
发表时间:
2018-05
影响因子:
2
通讯作者:
Zhu Qiankun
中科院分区:
文献类型:
--
作者:
Wang Zheng;Mao Zhihua;Xia Junshi;Du Peijun;Shi Liangliang;Huang Haiqing;Wang Tianyu;Gong Fang;Zhu Qiankun
The cloud cover for the South China Sea and its coastal area is relatively large throughout the year, which limits the potential application of optical remote sensing. A HJ-charge-coupled device (HJ-CCD) has the advantages of wide field, high temporal resolution, and short repeat cycle. However, this instrument suffers from its use of only four relatively low-quality bands which can’t adequately resolve the features of long wavelengths. The Landsat Enhanced Thematic Mapper-plus (ETM+) provides high-quality data, however, the Scan Line Corrector (SLC) stopped working and caused striping of remote sensed images, which dramatically reduced the coverage of the ETM+ data. In order to combine the advantages of the HJ-CCD and Landsat ETM+ data, we adopted a back-propagation artificial neural network (BP-ANN) to fuse these two data types for this study. The results showed that the fused output data not only have the advantage of data intactness for the HJ-CCD, but also have the advantages of the multi-spectral and high radiometric resolution of the ETM+ data. Moreover, the fused data were analyzed qualitatively, quantitatively and from a practical application point of view. Experimental studies indicated that the fused data have a full spatial distribution, multi-spectral bands, high radiometric resolution, a small difference between the observed and fused output data, and a high correlation between the observed and fused data. The excellent performance in its practical application is a further demonstration that the fused data are of high quality.
登录
查看更多内容
影响因子:
3.4
作者:
Yun-hao Chen;Deng Lei;L. Jing;Xiaobing Li;Peijun Shi
通讯作者:
Yun-hao Chen;Deng Lei;L. Jing;Xiaobing Li;Peijun Shi
DOI:
10.1109/icip.1997.638580
发表时间:
1997-10
期刊:
Proceedings of International Conference on Image Processing
影响因子:
--
作者:
Zhong-yu Chen;M. Desai;Xiao-Ping Zhang
通讯作者:
Zhong-yu Chen;M. Desai;Xiao-Ping Zhang
影响因子:
18.6
作者:
Liu, Z.;Blasch, E.;John, V.
通讯作者:
John, V.
影响因子:
13.5
作者:
Hilker, Thomas;Wulder, Michael A.;White, Joanne C.
通讯作者:
White, Joanne C.
影响因子:
2.3
作者:
A. Novelli;E. Tarantino;U. Fratino;V. Iacobellis;G. Romano;F. Gentile
通讯作者:
A. Novelli;E. Tarantino;U. Fratino;V. Iacobellis;G. Romano;F. Gentile