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A new sensor bias-driven spatio-temporal fusion model based on convolutional neural networks

Science China(Information Sciences) 2020年第4期63卷 24-39页

作者： Yunfei LI Jun LI Lin HE Jin CHEN Antonio PLAZA Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University School of Automation Science and Engineering South China University of Technology State Key Laboratory of Earth Surface Processes and Resource Ecology Institute of Remote Sensing Science and Engineering Faculty of Geographical ScienceBeijing Normal University Hyperspectral Computing Laboratory Department of Technology of Computers and CommunicationsEscuela Politécnica University of Extremadura

Owing to the tradeoff between scanning swath and pixel size, currently no satellite Earth observation sensors are able to collect images with high spatial and temporal resolution simultaneously. This limits the application of satellite images in many fields, including the characterization of crop yields or the detailed investigation of human-nature interactions. Spatio-temporal fusion(stf) is a widely used approach to solve the aforementioned problem. Traditional stf methods reconstruct fine-resolution images under the assumption that changes are able to be transferred directly from one sensor to another. However, this assumption may not hold in real scenarios, owing to the different capacity of available sensors to characterize changes. In this paper, we model such differences as a bias, and introduce a new sensor bias-driven stf model(called Bia stf) to mitigate the differences between the spectral and spatial distortions presented in traditional methods. In addition, we propose a new learning method based on convolutional neural networks(CNNs) to efficiently obtain this bias. An experimental evaluation on two public datasets suggests that our newly developed method achieves excellent performance when compared to other available approaches.

关键词： spatio-temporal fusion(stf) convolutional neural networks(CNNs) sensor bias-driven stf

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