[HTML][HTML] A review and meta-analysis of generative adversarial networks and their applications in remote sensing
Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Remote sensing data fusion with generative adversarial networks: State-of-the-art methods and future research directions
In the past decades, remote sensing (RS) data fusion has always been an active research
community. A large number of algorithms and models have been developed. Generative …
community. A large number of algorithms and models have been developed. Generative …
SEN12MS-CR-TS: A remote-sensing data set for multimodal multitemporal cloud removal
About half of all optical observations collected via spaceborne satellites are affected by haze
or clouds. Consequently, cloud coverage affects the remote-sensing practitioner's …
or clouds. Consequently, cloud coverage affects the remote-sensing practitioner's …
SpaceNet 6: Multi-sensor all weather mapping dataset
J Shermeyer, D Hogan, J Brown… - Proceedings of the …, 2020 - openaccess.thecvf.com
Within the remote sensing domain, a diverse set of acquisition modalities exist, each with
their own unique strengths and weaknesses. Yet, most of the current literature and open …
their own unique strengths and weaknesses. Yet, most of the current literature and open …
Deeply synergistic optical and SAR time series for crop dynamic monitoring
W Zhao, Y Qu, J Chen, Z Yuan - Remote Sensing of Environment, 2020 - Elsevier
Multi-temporal remote sensing imagery has been regarded as an effective tool to monitor
cropland. But optical sensors often miss key stages for crop growth because of clouds, which …
cropland. But optical sensors often miss key stages for crop growth because of clouds, which …
Restoration of motion-corrupted EEG signals using attention-guided operational CycleGAN
Electroencephalogram (EEG) signals suffer substantially from motion artifacts even in
ambulatory settings. Signal processing techniques for removing motion artifacts from EEG …
ambulatory settings. Signal processing techniques for removing motion artifacts from EEG …
[HTML][HTML] 深度学习图像数据增广方法研究综述
马岽奡， 唐娉， 赵理君， 张正 - 2021 - cjig.cn
摘要数据作为深度学习的驱动力, 对于模型的训练至关重要. 充足的训练数据不仅可以缓解模型
在训练时的过拟合问题, 而且可以进一步扩大参数搜索空间, 帮助模型进一步朝着全局最优解 …
在训练时的过拟合问题, 而且可以进一步扩大参数搜索空间, 帮助模型进一步朝着全局最优解 …
Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation
FN Darbaghshahi, MR Mohammadi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Satellite images are often contaminated by clouds. Cloud removal has received special
attention due to the wide range of satellite image applications. As the clouds thicken, the …
attention due to the wide range of satellite image applications. As the clouds thicken, the …
[HTML][HTML] Grassland mowing event detection using combined optical, SAR, and weather time series
AK Holtgrave, F Lobert, S Erasmi, N Röder… - Remote Sensing of …, 2023 - Elsevier
Abstract The European Union's Common Agricultural Policy (CAP) and the Habitats
Directive aim to improve biodiversity in agricultural landscapes. Both policies require …
Directive aim to improve biodiversity in agricultural landscapes. Both policies require …
Modality translation in remote sensing time series
Modality translation, which aims to translate images from a source modality to a target one,
has attracted a growing interest in the field of remote sensing recently. Compared to …
has attracted a growing interest in the field of remote sensing recently. Compared to …