Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea

Abstract

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.

Keywords

data fusion / South China Sea / BP-ANN model

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Acknowledgements

The authors would like to thank the reviewers and the editor for their constructive comments. This study was supported by the National Key Research and Development Program of China (2016YFC1400901), the High Resolution Earth Observation Systems of National Science and Technology Major Projects (41-Y20A31-9003-15/17), the National Natural Science Foundation of China (Grant Nos. 41476156 and 41621064), and the Public Science and Technology Research Funds Projects of Ocean (201005030).

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