Accurate and lightweight cloud detection method based on cloud and snow coexistence region of high-resolution remote sensing images

doi:10.11947/j.AGCS.2023.20210686

Abstract

Abstract: Cloud detection is a critical stage in remote sensing image preprocessing. However, when there is snow on the underlying surface of scenes, the general cloud detection methods wouldbe easily affected. As a result, the cloud detection accuracy of these methods would reduce.Furthermore, most available cloud detection datasets are of medium-resolution and do not focus on the cloud and snow coexistence study areas. As a result, a cloud detection dataset has been created and released based on high-resolution cloud-snow coexistence remote sensing images.Meanwhile, this study suggests a convolution neural network termed RDC-Net for cloud detection in high-resolution cloud and snow coexistence images. The RDC-Net contains the reconstructible multiscale feature fusion module for multiscale cloud feature extraction, the dual adaptive feature fusion module for effective cloud feature representation reconstruction, and the controllably deep gradient guidance flows module for unbiased network gradient descent guidance. Benefiting from the above technical components, the network can enhance the robustness of cloud detection in complicated regions and facilitate lightweight deployment of the network. The experimental results show that the RDC-Net has an excellent anti-interference capacity for highlighted ground objects and has outstanding detection performance for thin clouds and clouds over snow. Furthermore, the RDC-Net has fewer parameters and floating-point operations, making it acceptable for industrial production and application.

Key words: high-resolution remote sensing images, cloud and snow coexistence region, cloud detection, convolutional neural network, high accuracy, lightweight

CLC Number:

P237

ZHANG Guangbin, GAO Xianjun, RAN Shuhao, YANG Yuanwei, LI Lishan, ZHANG Yan. Accurate and lightweight cloud detection method based on cloud and snow coexistence region of high-resolution remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(1): 93-107.

References

[1] IDSO S B, JACKSON R D, REGINATO R J. Remote-sensing of crop yields[J]. Science (New York, N Y), 1977, 196(4285): 19-25.
[2] 郑凯, 李建胜, 杨戬峰, 等. 天绘一号卫星遥感影像云雪检测的ResNet与DeepLabV³⁺综合法[J]. 测绘学报, 2020, 49(10): 1343-1353. DOI: 10.11947/j.AGCS.2020.20190420. ZHENG Kai, LI Jiansheng, YANG Jianfeng, et al. A cloud and snow detection method of TH-1 image based on combined ResNet and DeepLabV³⁺[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(10): 1343-1353.DOI: 10.11947/j.AGCS.2020.20190420.
[3] 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.
[4] ZHAN Yongjie, WANG Jian, SHI Jianping, et al. Distinguishing cloud and snow in satellite images via deep convolutional network[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1785-1789.
[5] IRISH R R. Landsat 7 automatic cloud cover assessment[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2000, 4049: 348-355.
[6] ZHU Zhe, WOODCOCK C E. Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment, 2012, 118: 83-94.
[7] 王永吉, 明艳芳, 梁天辰, 等. 基于改进LCCD算法的高分六号WFV数据云检测研究[J]. 光学学报, 2020, 40(21): 169-180. WANG Yongji, MING Yanfang, LIANG Tianchen, et al. GF-6 WFV data cloud detection based on improved LCCD algorithm[J]. Acta Optica Sinica, 2020, 40(21): 169-180.
[8] WIELAND M, LI Yu, MARTINIS S. Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network[J]. Remote Sensing of Environment, 2019, 230: 111203.
[9] LI Pengfei, DONG Limin, XIAO Huachao, et al. A cloud image detection method based on SVM vector machine[J]. Neurocomputing, 2015, 169: 34-42.
[10] GHASEMIAN N, AKHOONDZADEH M. Introducing two random forest based methods for cloud detection in remote sensing images[J]. Advances in Space Research, 2018, 62(2): 288-303.
[11] HUGHES M, HAYES D. Automated detection of cloud and cloud shadow in single-date landsat imagery using neural networks and spatial post-processing[J]. Remote Sensing, 2014, 6(6): 4907-4926.
[12] CHAI Dengfeng, 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.
[13] 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.
[14] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015: 234-241.
[15] LI Zhiwei, SHEN Huanfeng, CHENG Qing, et al. Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150: 197-212.
[16] GUO Jianhua, YANG Jingyu, YUE Huanjing, et al. CDnetV2: CNN-based cloud detection for remote sensing imagery with cloud-snow coexistence[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 700-713.
[17] HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA:IEEE, 2017: 2261-2269.
[18] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA:IEEE, 2015: 1-9.
[19] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA:IEEE, 2016: 770-778.
[20] ZHANG Guangbin, GAO Xianjun, YANG Yuanwei, et al. Controllably deep supervision and multi-scale feature fusion network for cloud and snow detection based on medium- and high-resolution imagery dataset[J]. Remote Sensing, 2021, 13(23): 4805.
[21] DING Xiaohan, ZHANG Xiangyu, MA Ningning, et al. RepVGG: making VGG-style ConvNets great again[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA:IEEE, 2021: 13728-13737.
[22] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2017, 31(1): 4278-4284.
[23] WANG Qilong, WU Banggu, ZHU Pengfei, et al. ECA-net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA: IEEE,2020: 11531-11539.
[24] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of 2018 Computer Vision-ECCV 2018.Cham: Springer,2018: 3-19.
[25] FOGA S, SCARAMUZZA P L, GUO Song, et al. Cloud detection algorithm comparison and validation for operational Landsat data products[J]. Remote Sensing of Environment, 2017, 194: 379-390.
[26] MOHAJERANI S, SAEEDI P. Cloud and cloud shadow segmentation for remote sensing imagery via filtered jaccard loss function and parametric augmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 4254-4266.
[27] 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.
[28] VOIGT S, GIULIO-TONOLO F, LYONS J, et al. Global trends in satellite-based emergency mapping[J]. Science (New York, N Y), 2016, 353(6296): 247-252.