An NDVI synthesis method for multi-temporal remote sensing images based on k–NN learning: a case based on GF-1 data

ABSTRACT

Gaofen-1 (GF-1) satellite data has the advantages of having high temporal resolution and wide coverage. Therefore, normalised difference vegetation index (NDVI) data collected by GF-1 can provide an accurate assessment of vegetation coverage. Because of its limited range and the interference of cloud cover, NDVI data should be synthesised by using multi-day data, which creates a more reliable dataset. However, NDVI data synthesised by existing methods have poor continuity and reliability. To overcome these problems, an NDVI synthesis method for multi-temporal remote sensing images based on k-nearest neighbour (k-NN) learning is proposed in the present study. Based on a k-NN learning algorithm and the continuity in spatial and temporal aspects of NDVI data, multi-temporal remote sensing image data was screened and classified to remove cloud cover. Then, each image was assigned a weight, based on which the data weighting fusion could be achieved. Compared with the Maximum Value Compositing and the Average Compositing methods, the k-NN method proposed in this study was found to remove the mutation points more effectively, ensuring better spatial continuity of NDVI data and improving the reliability of the results.

Acknowledgments

We would like to thank LetPub (www.LetPub.com) for providing linguistic assistance during the preparation of this manuscript.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Project No. 41701531. It was also supported in part by the Natural Science Foundation of Jiangsu Province under Project No. BK20170782 and by the Open Research Fund of the State Key Laboratory of Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing under grant No. 2016-ZW-KFJJ-01.