Abstract
Intelligence method to detect changes in remote sensing images is a difficult but important issue and it is of great significance for natural resources, environmental and social-economy monitoring. In this paper, we presented a novel deep learning model named PSPNet-CONC which combined multi-scale feature deep learning model PSPNet and features extraction module ResNet34 in multi-period remote sensing images. Experiments were designed and conducted systematically for the comparison between the deep learning methods and traditional methods. Our experimental accuracy results show that our model got at least 11% higher in recall index than other state-of-the-art methods. Further more PA also increase by 4.5%, and unchanged accuracy is 1% better than other excellence methods. It demonstrates that with the characteristic of deep learning with multi-scale information, the PSPNet-CONC model could generate higher accuracy and stability detection results than other methods.
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Yu, X., Fan, J., Zhang, P., Han, L., Zhang, D., Sun, G. (2020). Multi-scale Convolutional Neural Network for Remote Sensing Image Change Detection. In: Xie, Y., Li, Y., Yang, J., Xu, J., Deng, Y. (eds) Geoinformatics in Sustainable Ecosystem and Society. GSES GeoAI 2019 2019. Communications in Computer and Information Science, vol 1228. Springer, Singapore. https://doi.org/10.1007/978-981-15-6106-1_18
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