Spatiotemporal Fusion of Remote Sensing Image Based on Deep Learning

Table 1

Quantitative assessment for the Coleambally dataset.

	Bands	Fit-FC	STDFA	FSDAF	STI-FM	HCM	STARFM	Our method
RMSE	Blue	0.0088	0.0089	0.0091	0.0098	0.0094	0.0086	0.0081
	Green	0.0113	0.0115	0.0117	0.0124	0.0120	0.0110	0.0105
	Red	0.0150	0.0152	0.0154	0.0164	0.0159	0.0146	0.0139
	NIR	0.0240	0.0244	0.0248	0.0268	0.0258	0.0233	0.0219
	SWIR1	0.0328	0.0331	0.0339	0.0363	0.0351	0.0318	0.0299
	SWIR2	0.0308	0.0317	0.0319	0.0344	0.0331	0.0299	0.0279
	Mean	0.0205	0.0208	0.0211	0.0227	0.0219	0.0199	0.0187
CC	Blue	0.8761	0.8773	0.8674	0.8463	0.8574	0.8833	0.8967
	Green	0.8786	0.8928	0.8705	0.8510	0.8612	0.8853	0.8976
	Red	0.8925	0.8931	0.8856	0.8687	0.8775	0.8982	0.9085
	NIR	0.7713	0.7722	0.7550	0.7171	0.7367	0.7850	0.8098
	SWIR1	0.8327	0.8331	0.8207	0.7925	0.8072	0.8429	0.8627
	SWIR2	0.8393	0.8398	0.8269	0.7978	0.8130	0.8497	0.8697
	Mean	0.8484	0.8489	0.8377	0.8122	0.8255	0.8574	0.8742
UIQI	Blue	0.8653	0.8662	0.8564	0.8350	0.8462	0.8728	0.8867
	Green	0.8680	0.8687	0.8597	0.8400	0.8503	0.8750	0.8879
	Red	0.8835	0.8841	0.8763	0.8591	0.8681	0.8895	0.9008
	NIR	0.7644	0.7652	0.7488	0.7125	0.7313	0.7775	0.8011
	SWIR1	0.8251	0.8259	0.8132	0.7855	0.7999	0.8352	0.8557
	SWIR2	0.8313	0.8319	0.8190	0.7903	0.8053	0.8418	0.8622
	Mean	0.8396	0.8403	0.8289	0.8037	0.8168	0.8486	0.8657