Abstract:
Objectives The problem of missing data in the monthly nocturnal visible infrared imaging radiometer suite (VIIRS) remote sensing images has become one of the limitations for application. Exploring practical data processing methods to obtain high-quality spatiotemporal continuous VIIRS monthly image data has become necessary for night time light remote sensing research.
Methods A spatiotemporal interpolation method of image elements using the correlation of feature proximity relationship is proposed. The image element data with good consistency of temporal variation is used as the reference for spatial interpolation. The monthly data with good consistency of spatial relationship is used as the reference for temporal interpolation, and the spatiotemporal interpolation of the image element to be interpolated is obtained by constructing different convolution kernels to convolve the preliminary interpolation results in temporal and spatial dimensions, respectively.
Results Taking the monthly night time light remote sensing image restoration of Jiangsu Province in 2015 as an example, we compare and analyze the spatiotemporal interpolation methods in different dimensions. Compared with the three Hermit interpolations, the maximum relative error of the monthly image light brightness sum, the relative error of the annual image light brightness sum, and the image-by-image difference obtained by the spatiotemporal interpolation method are significantly reduced.
Conclusions The proposed spatiotemporal interpolation method takes both the temporal trend smoothness and spatial structure stability of VIIRS data into account in the interpolation process, and the image interpolation accuracy is significantly improved. The proposed method is more extensive because there is no strict requirement for the time series data before and after the month to be interpolated.
下载:
