A hybrid deep learning model for spatiotemporal image fusion

We have recently developed an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, […]

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A hybrid phenology matching model for crop phenology retrieval

We develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models, and can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois. The paper […]

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Prof. Diao receives NSF CAREER Award

Prof. Diao has been awarded a National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) Award for her project entitled “CAREER: Scalable Remote Sensing Computational Framework for Near-real-time Crop Characterization.” The CAREER Award is highly competitive and indicates a faculty member’s potential to serve as an academic role model in research and education.

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