Deep learning methods have been developed and widely used in land use classification with remote sensing images. In addition, due to the different datasets used in different studies, there is a lack of direct comparison between different deep learning model applications in land use classification. The open source dataset DeepSat was used to build and test a convolutional neural network (CNN) model. The convolution kernels in the model were extracted to further study the specific features learned by the deep learning model. In addition, different CNN-based models were compared to explore the impacts of model structures on model accuracy. The major conclusions from the research are: (1) CNN model is effective in land use classification, with an accuracy of 0.9998 and 0.9991 for the SAT-4 and SAT-6 data, respectively; (2) CNN does have a “learning” ability that can extract the most critical and effective information from training datasets; and (3) for remote sensing land use classification, increasing the number of convolution kernels is better than adding more convolutional layers. Max pooling is better than average pooling. In addition, a localized response normalized layer can also improve model accuracy.
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