Abstract
To improve the classification accuracy in remote sensing image processing, this paper proposes a remote sensing image classification method based on depth residual neural network. Because the deep learning model itself needs a lot of training data, the traditional method of rotation and clipping is needed to expand the image data. A large number of manual operations, the efficiency improvement is not obvious, so this paper draws lessons from the idea of migration learning, pretrains on large-scale data set ImageNet, gets the inverted initial model, then trains on UCM_LandUse_21 with the pretrained model, and optimizes the training strategy to obtain the ideal model. The experimental results show that compared with the traditional classification method, the proposed method improves the classification accuracy more obviously, reaching 97.87%, thus verifying the effectiveness of the method.
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Li, L., Li, D. (2020). Scene Classification of Remote Sensing Images Based on ResNet. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-32-9686-2_40
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DOI: https://doi.org/10.1007/978-981-32-9686-2_40
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