Applying self-supervised learning for semantic cloud segmentation of all-sky images
暂无分享,去创建一个
Robert Pitz-Paal | Rudolph Triebel | Stefan Wilbert | Bijan Nouri | Luis F. Zarzalejo | Pascal Moritz Kuhn | Yann Fabel | Niklas Blum | Marcel Hasenbalg | L. Zarzalejo | S. Wilbert | Rudolph Triebel | R. Pitz-Paal | B. Nouri | Yann Fabel | N. Blum | Pascal Kuhn | M. Hasenbalg | R. Pitz‐Paal
[1] Bijan Nouri,et al. Benchmarking of six cloud segmentation algorithms for ground-based all-sky imagers , 2020 .
[2] Pu Liu,et al. An Efficient Solution for Semantic Segmentation of Three Ground‐based Cloud Datasets , 2020 .
[3] Chunheng Wang,et al. Automatic Cloud Detection for All-Sky Images Using Superpixel Segmentation , 2015, IEEE Geoscience and Remote Sensing Letters.
[4] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Hsu-Yung Cheng,et al. Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques , 2016 .
[6] Zhiguo Cao,et al. Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[7] Josep Calbó,et al. Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images , 2006 .
[8] Ming-Hsuan Yang,et al. Unsupervised Representation Learning by Sorting Sequences , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[9] Christian Ledig,et al. Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize , 2017, ArXiv.
[10] Stefan Winkler,et al. Multi-level semantic labeling of Sky/cloud images , 2015, 2015 IEEE International Conference on Image Processing (ICIP).
[11] Paolo Favaro,et al. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles , 2016, ECCV.
[12] Liu Xinwu. This is How the Discussion Started , 1981 .
[13] Leslie N. Smith,et al. A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay , 2018, ArXiv.
[14] R. Pitz-Paal,et al. Determination of cloud transmittance for all sky imager based solar nowcasting , 2019, Solar Energy.
[15] Matthijs Douze,et al. Deep Clustering for Unsupervised Learning of Visual Features , 2018, ECCV.
[16] W. Rossow,et al. Advances in understanding clouds from ISCCP , 1999 .
[17] Chunheng Wang,et al. Ground-Based Cloud Detection Using Graph Model Built Upon Superpixels , 2017, IEEE Geoscience and Remote Sensing Letters.
[18] A. Heinle,et al. Automatic cloud classification of whole sky images , 2010 .
[19] Jeffrey J. Rodriguez,et al. A New Contrast-Enhancing Feature for Cloud Detection in Ground-Based Sky Images , 2015 .
[20] Zhiguo Cao,et al. Cloud Classification of Ground-Based Images Using Texture–Structure Features , 2014 .
[21] Daniel Rowe,et al. Short-term irradiance forecasting using skycams: Motivation and development , 2014 .
[22] Stefan Wilbert,et al. Application of Whole Sky Imagers for Data Selection for Radiometer Calibration , 2016 .
[23] C. Long,et al. Total Sky Imager Model 880 Status and Testing Results , 2001 .
[24] Yingli Tian,et al. Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Baihua Xiao,et al. Ground-Based Cloud Detection Using Automatic Graph Cut , 2015, IEEE Geoscience and Remote Sensing Letters.
[26] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[27] Jun Yang,et al. A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images , 2011 .
[28] J. Augustine,et al. The thin border between cloud and aerosol: Sensitivity of several ground based observation techniques , 2017 .
[29] Sebastian Ruder,et al. Universal Language Model Fine-tuning for Text Classification , 2018, ACL.
[30] J. Shields,et al. The Whole Sky Imager - A Year of Progress , 1998 .
[31] J. Kleissl,et al. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed , 2011 .
[32] William B. Rossow,et al. Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets: 2. Validation and first results , 1995 .
[33] Stefan Wilbert,et al. Short-term forecasting of high resolution local DNI maps with multiple fish-eye cameras in stereoscopic mode , 2017 .
[34] Yoram J. Kaufman,et al. On the twilight zone between clouds and aerosols , 2007 .
[35] Enio Bueno Pereira,et al. A Simple Method for the Assessment of the Cloud Cover State in High-Latitude Regions by a Ground-Based Digital Camera , 2006 .
[36] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[37] Stéphane Grieu,et al. Cloud Detection Methodology Based on a Sky-imaging System☆ , 2015 .
[38] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[39] Feng Zhang,et al. CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network , 2018, Geophysical Research Letters.
[40] Fabio Del Frate,et al. Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images , 2015, IEEE Geoscience and Remote Sensing Letters.
[41] Alexei A. Efros,et al. Unsupervised Visual Representation Learning by Context Prediction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[42] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[43] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[45] Stefan Winkler,et al. Systematic study of color spaces and components for the segmentation of sky/cloud images , 2014, 2014 IEEE International Conference on Image Processing (ICIP).
[46] Mathieu David,et al. Spatial and Temporal Variability of Solar Energy , 2016 .
[47] Zhenzhu Wang,et al. SegCloud: a novel cloud image segmentation model using deep Convolutional Neural Network for ground-based all-sky-view camera observation , 2019 .
[48] Robert Pitz-Paal,et al. Validation of an all‐sky imager–based nowcasting system for industrial PV plants , 2018 .
[49] Leslie N. Smith,et al. Cyclical Learning Rates for Training Neural Networks , 2015, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[50] H. Chepfer,et al. Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel , 2013 .
[51] C. W. Chow,et al. A method for cloud detection and opacity classification based on ground based sky imagery , 2012 .
[52] Zhiguo Cao,et al. DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[53] George Economou,et al. Cloud detection and classification with the use of whole-sky ground-based images , 2012 .
[54] Stefan Winkler,et al. Multi-label Cloud Segmentation Using a Deep Network , 2019, 2019 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium).
[55] Robert Pitz-Paal,et al. Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts , 2019, Remote. Sens..
[56] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.