MS or MS + PAN is usually applied separately in convolutional neural network ( CNN) resolution reconstruction to obtain high-resolution MS images，but the difference between the two datasets is rarely studied．This paper introduced a dual-channel network and took MS and MS + PAN of Jilin-1 spectrum satellites as two
datasets to evaluate the performance of CNN resolution reconstruction，and analyzed the difference with bicubic and GS methods． The result of CNN reconstruction shows that MS + PAN dataset performed better than MS，
with about 6% improvement in spatial and spectral components，and the overall quality of MS + PAN dataset was slightly higher than that of MS dataset，with QNＲ from 0． 955 9 to 0． 958 4． The bicubic performed best in spectral components with the quality value of 0． 017，and GS performed best in spatial components with the quality values of 0． 044 3． CNN showed similar performance in spectral and spatial components with the two traditional methods and achieved the best overall quality with QNＲ value of 0． 958 4．
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