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-Net: Superresolving SAR Tomographic Inversion via Deep Learning
Kun Qian, Yuanyuan Wang, Yilei Shi +1
Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (…
A Survey of Uncertainty in Deep Neural Networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali +11
Due to their increasing spread, confidence in neural network predictions became more and more important. However, basic neural networks do not deliver certainty estimates or suffer…
Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model
Danfeng Hong, Jingliang Hu, Jing Yao +2
As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing i…
A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features
Jun Li, Zhaocong Wu, Zhongwen Hu +5
Clouds are a very important factor in the availability of optical remote sensing images. Recently, deep learning-based cloud detection methods have surpassed classical methods base…
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Konstantin Klemmer, Sudipan Saha, Matthias Kahl +2
Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots…
Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks
Yuansheng Hua, Lichao Moua, Jianzhe Lin +2
Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to c…