A reconstruction method for remote sensing missing data considering spatial heterogeneity and noise
In response to the common issue of extensive missing data in optical remote sensing data and the insufficient consideration of the spatial correlation of geographic data in existing algorithms for data reconstruction,this paper fully utilizes the spatio-temporal correlation between geographic spatial data and proposes a reconstruction method that combines random forest(RF)and geographically weighted regression(GWR),termed as RF+GWR.Using normalized difference vegetation index(NDVI)from GF-4,MODIS land surface temperature(LST),and GF-4 reflectance data as experimental materials,the universality and missing data reconstruction performance of the RF+GWR method are evaluated.Experimental results show that,under different cloud masking levels as set in the paper,compared to K-nearest neighbor(KNN)and RF methods,the RF+GWR method exhibits varying degrees of improvement in reconstructing missing data of GF-4 NDVI,MODIS LST,and GF-4 band reflectance data.The maximum improvements in root mean square error,mean absolute error,and coefficient of determination are 33.07％,30.19％,and 7.06％.
optical remote sensingmissing data reconstructiongeographically weighted regressionrandom forestK-nearest neighbor
万方数据
维普
