Remote sensing image scene classification based on Inception-V3
CAI Zhiling1(), WENG Qian1(), YE Shaozhen1, JIAN Cairen2
1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China 2. School of Information Science and Technology, Xiamen University Tan Kahkee College, Zhangzhou 363105, China
With the deepening and cross-fusion of modern remote sensing image research, the classification of high spatial resolution remote sensing image (referred to as “high-resolution image”) has become a research hotspot in the field of remote sensing. As the phenomenon of “homology spectrum” and “homology spectrum” of high-resolution image is more serious, the deep learning method that has emerged in recent years has proposed a new solution for high-resolution image classification. However, the lack of training samples of remote sensing images can easily lead to over-fitting of deep learning networks. In this paper, an improved Inception-V3 remote sensing image scene classification model is proposed by using deep learning method and transfer learning strategy. The model first adds Dropout layer before the full connection layer of the original Inception-V3 model in order to avoid over-fitting. In the training process, the transfer learning strategy is adopted to make full use of the existing model and knowledge and improve the training efficiency. The experimental results based on AID and NWPU-RESISC45 datasets show that the improved Inception-V3 has faster convergence speed and smoother training effect than the original Inception-V3 training. Compared with accuracy of other traditional methods and deep learning networks, the classification accuracy of the proposed model has been greatly improved and verified. The effectiveness of the model is verified.
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CAI Zhiling, WENG Qian, YE Shaozhen, JIAN Cairen. Remote sensing image scene classification based on Inception-V3. Remote Sensing for Land & Resources, 2020, 32(3): 80-89.
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2020, Vol. 32 
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