Spatiotemporal Fusion of Remote Sensing Image Based on Deep Learning

Table 2

Quantitative assessment for the second dataset.

	Bands	ESTARFM	SPSTFM	StfNet	Our method
RMSE	Blue	0.0118	0.0112	0.0109	0.0104
	Green	0.0139	0.0132	0.0128	0.0122
	Red	0.0217	0.0208	0.0203	0.0196
	NIR	0.0361	0.0332	0.0317	0.0301
	SWIR1	0.0306	0.0288	0.0279	0.0268
	SWIR2	0.0369	0.0356	0.0348	0.0340
	Mean	0.0252	0.0238	0.0231	0.0222
CC	Blue	0.8512	0.8727	0.8837	0.8964
	Green	0.8520	0.8729	0.8833	0.8951
	Red	0.8537	0.8756	0.8864	0.8990
	NIR	0.7131	0.7601	0.7824	0.8055
	SWIR1	0.8519	0.8735	0.8843	0.8955
	SWIR2	0.8725	0.8900	0.8987	0.9077
	Mean	0.8324	0.8575	0.8698	0.8832
UIQI	Blue	0.8425	0.8622	0.8723	0.8847
	Green	0.8431	0.8624	0.8719	0.8834
	Red	0.8297	0.8483	0.8574	0.8688
	NIR	0.7040	0.7500	0.7714	0.7940
	SWIR1	0.8455	0.8655	0.8753	0.8858
	SWIR2	0.8529	0.8667	0.8735	0.8810
	Mean	0.8196	0.8425	0.8537	0.8663