The extraction of cage aquacultural areas was investigated using high-resolution GF-1 and GF-2 remote sensing images from northern Fujian Province. Image enhancement was performed by correction, fusion, and cropping. The sample database of inland cage culture areas of two kinds of images was constructed; The sample bank is used to train the in-depth learning fully convolutional networks (FCN) model extracted from inland cage culture area and verify the accuracy. The results of the test experiment show that the F-measure of GF-1 and GF-2 reaches 83.37% and 92.56%,respectively. It shows that the inland cage culture area extraction based on FCN has high accuracy, and can be used for large-scale inland cage acquaculture area extraction, which provides an important basis for the monitoring of inland aquaculture area.