|
Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (3): 622-633.doi: 10.1007/s11390-019-1931-y
Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia
• Artificial Intelligence and Pattern Recognition • Previous Articles Next Articles
Han Liu1,2, Hang Du1,2, Dan Zeng1,2,*, Qi Tian3, Fellow, IEEE
[1] Xie F, Shi M, Shi Z, Yin J, Zhao D. Multilevel cloud detection in remote sensing images based on deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8):3631-3640. [2] Shi M, Xie F, Zi Y, Yin J. Cloud detection of remote sensing images by deep learning. In Proc. the 2016 IEEE International Geoscience and Remote Sensing Symposium, July 2016, pp.701-704. [3] Liu H, Zeng D, Tian Q. Super pixel cloud detection using hierarchical fusion CNN. In Proc. the 4th IEEE International Conference on Multimedia Big Data, September 2018, Article No. 32. [4] Zhou Z H, Feng J. Deep forest:Towards an alternative to deep neural networks. arXiv:1702.08835, 2017. https://arxiv.org/abs/1702.08835, May 2018. [5] Jedlovec G J, Haines S L, LaFontaine F J. Spatial and temporal varying thresholds for cloud detection in GOES imagery. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6):1705-1717. [6] Zhang Q, Xiao C. Cloud detection of RGB color aerial photographs by progressive refinement scheme. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11):7264-7275. [7] Zhong B, Chen W, Wu S, Hu L, Luo X, Liu Q. A cloud detection method based on relationship between objects of cloud and cloud-shadow for Chinese moderate to high resolution satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(11):4898-4908. [8] Li Z, Shen H, Li H, Xia G, Gamba P, Zhang L. Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery. Remote Sensing of Environment, 2017, 191:342-358. [9] Visa A, Valkealahti K, Simula O. Cloud detection based on texture segmentation by neural network methods. In Proc. the 1991 IEEE International Joint Conference on Neural Networks, Vol. 2, November 1991, pp.1001-1006. [10] Zhan Y, Wang J, Shi J, Cheng G, Yao L, Sun W. Distinguishing cloud and snow in satellite images via deep convolutional network. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10):1785-1789. [11] Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2274-2282. [12] Hu X, Wang Y, Shan J. Automatic recognition of cloud images by using visual saliency features. IEEE Geosci. Remote Sensing Lett., 2015, 12(8):1760-1764. [13] Tan K, Zhang Y, Tong X. Cloud extraction from Chinese high resolution satellite imagery by probabilistic latent semantic analysis and object-based machine learning. Remote Sensing, 2016, 8(11):Article No. 963. [14] Yuan Y, Hu X. Bag-of-words and object-based classification for cloud extraction from satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8):4197-4205. [15] Li Z, Shen H, Wei Y, Cheng Q, Yuan Q. Cloud detection by fusing multi-scale convolutional features. In Proc. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Science, May 2018, pp.149-152. [16] Ma C, Luo G, Wang K. Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Transactions on Medical Imaging, 2018, 37(8):1943-1954. [17] Zhang C, Yan J, Li C, Bie R. Contour detection via stacking random forest learning. Neurocomputing, 2018, 275:2702- 2715. [18] Shen Y, Lu H, Jia J. Classification of motor imagery EEG signals with deep learning models. In Proc. the 7th International Conference on Intelligent Science and Big Data Engineering, September 2017, pp.181-190. [19] Dollár P, Zitnick C L. Fast edge detection using structured forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(8):1558-1570. [20] van den Bergh M, Boix X, Roig G, van Gool L. SEEDS:Superpixels extracted via energy-driven sampling. International Journal of Computer Vision, 2015, 111(3):298-314. [21] Krizhevsky A. Learning multiple layers of features from tiny images. Technical Report, University of Toronto, 2009. http://www.cs.toronto.edu/~kriz/learning-features- 2009-TR.pdf, March 2019. [22] Lin T Y, Dollár P, Girshick R B, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.936-944. [23] Chen L C, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611, 2018. https://arxiv.org/abs/1802.02611, March 2019. [24] Chen L C, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587, 2017. https://arxiv.org/abs/1706.05587, March 2019. [25] Chollet F. Xception:Deep learning with depthwise separable convolutions. arXiv:1610.02357, 2016. https://arxiv.org/abs/1610.02357, March 2019. [26] Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. Caffe:Convolutional architecture for fast feature embedding. In Proc. the 22nd ACM International Conference on Multimedia, November 2014, pp.675-678. [27] Tang M, Gorelick L, Veksler O, Boykov Y. GrabCut in one cut. In Proc. the 2013 IEEE International Conference on Computer Vision, December 2013, pp.1769-1776. |
No related articles found! |
|
|