针对目前用于遥感图像云检测的神经网络模型存在光谱信息未能充分利用而导致的细节信息易损失、碎云漏检率大、计算复杂等不足，提出一种新型且轻量的网络，称为勺型网络（spoon-net，S-Net），应用于Landsat遥感图像的云检测。S-Net分为2个阶段，第1阶段，使用1×1的卷积核提取图像光谱特征，避免图像细节被模糊；第2阶段，使用encoder-decoder框架提取图像空间特征，并引入分组卷积，对第1阶段提取的每一层光谱通道单独进行卷积，保持光谱特征并减少模型参数。模型在Landsat8 biome数据训练测试并评估，结果表明模型在内存与时间上具有较大优势，并达到95%的准确率。
In view of the shortcomings of the neural network model for remote sensing image cloud detection, such as the loss of detail information, the high cloud miss detection rate and the complexity of calculation caused by the insufficient utilization of spectral information, this paper proposes a new and lightweight network called spoon net (S-Net), which is applied to the cloud detection of Landsat remote sensing image. S-Net is divided into two stages. In the first stage, the convolution kernel of 1×1 is used to extract image spectral features to avoid image details being blurred; in the second stage, the encoder decoder framework is used to extract image spatial features, and group convolution is introduced to convolute each layer of spectral channels extracted in the first stage separately to maintain spectral features and reduce model parameters. The model is trained and evaluated in Landsat8 biome dataset, and the results show that the model has a great advantage in memory and time, and achieves an accuracy of 95%.
[1] Prasad A K, Chai L, Singh R P, et al. Crop yield estimation model for Iowa using remote sensing and surface parameters[J]. International Journal of Applied Earth Observation and Geoinformation, 2006, 8(1):26-33.DOI:10.1016/j.jag.2005.06.002.
[2] Verbesselt J, Hyndman R, Newnham G, et al. Detecting trend and seasonal changes in satellite image time series[J]. Remote Sensing of Environment, 2010, 114(1):106-115.DOI:10.1016/j.rse.2009.08.014.
[3] Joyce K E, Belliss S E, Samsonov S V, et al. A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters[J]. Progress in Physical Geography:Earth and Environment, 2009, 33(2):183-207.DOI:10.1177/0309133309339563.
[4] Ju J C, Roy D P. The availability of cloud-free Landsat ETM+data over the conterminous United States and globally[J]. Remote Sensing of Environment, 2008, 112(3):1196-1211.DOI:10.1016/j.rse.2007.08.011.
[5] Sun L, Liu X Y, Yang Y K, et al. A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 138:193-207.DOI:10.1016/j.isprsjprs.2018.02.016.
[6] Irish R R, Barker J L, Goward S N, et al. Characterization of the Landsat-7 ETM+automated cloud-cover assessment (ACCA) algorithm[J]. Photogrammetric Engineering & Remote Sensing, 2006, 72(10):1179-1188.DOI:10.14358/pers.72.10.1179.
[7] Zhu Z, Woodcock C E. Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment, 2012, 118:83-94.DOI:10.1016/j.rse.2011.10.028.
[8] Achanta R, Shaji A, Smith K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2274-2282.DOI:10.1109/TPAMI.2012.120.
[9] Lee Y, Wahba G, Ackerman S A. Cloud classification of satellite radiance data by multicategory support vector machines[J]. Journal of Atmospheric and Oceanic Technology, 2004, 21(2):159-169.DOI:10.1175/1520-0426(2004)021<0159:ccosrd>2.0.co;2.
[10] Tian B, Shaikh M A, Azimi-Sadjadi M R, et al. A study of cloud classification with neural networks using spectral and textural features[J]. IEEE Transactions on Neural Networks, 1999, 10(1):138-151.DOI:10.1109/72.737500.
[11] Jeppesen J H, Jacobsen R H, Inceoglu F, et al. A cloud detection algorithm for satellite imagery based on deep learning[J]. Remote Sensing of Environment, 2019, 229:247-259.DOI:10.1016/j.rse.2019.03.039.
[12] Chai D F, Newsam S, Zhang H K, et al. Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks[J]. Remote Sensing of Environment, 2019, 225:307-316.DOI:10.1016/j.rse.2019.03.007.
[13] Hughes M J, Kennedy R. High-quality cloud masking of Landsat 8 imagery using convolutional neural networks[J]. Remote Sensing, 2019, 11(21):2591.DOI:10.3390/rs11212591.
[14] Zhang X D, Wang T, Chen G Z, et al. Convective clouds extraction from Himawari-8 satellite images based on double-stream fully convolutional networks[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(4):553-557.DOI:10.1109/LGRS.2019.2926402.
[15] Foga S, Scaramuzza P L, Guo S, et al. Cloud detection algorithm comparison and validation for operational Landsat data products[J]. Remote Sensing of Environment, 2017, 194:379-390.DOI:10.1016/j.rse.2017.03.026.
[16] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. June 7-12, 2015, Boston, MA, USA. IEEE, 2015:3431-3440.DOI:10.1109/CVPR.2015.7298965.
[17] Ronneberger O, Fischer P, Brox T. U-Net:convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Springer, Cham, 2015:234-241. DOI:10.1007/978-3-319-24574-4_28.
[18] Ioffe S, Szegedy C. Batch normalization:accelerating deep network training by reducing internal ovariate shift[J]. arXiv preprint arXiv:502.03167, 2015.
[19] Badrinarayanan V, Kendall A, Cipolla R. Segnet:a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12):2481-2495.DOI:10.1109/TPAMI.2016.2644615.
[20] Scaramuzza P L, Bouchard M A, Dwyer J L. Development of the Landsat data continuity mission cloud-cover assessment algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4):1140-1154.DOI:10.1109/TGRS.2011.2164087.
