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所属单位：航天学院

发表刊物：REMOTE SENSING LETTERS

关键字：TIME-SERIES NEAREST-NEIGHBOR VEGETATION

摘要：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.

ISSN号：2150-704X

是否译文：否

发表时间：2018-01-01

合写作者：Bao, Jianwei,Peng, Jiefei,Zhang, Jie,Wang, Ping

通讯作者：王博,Wang, Ping