Abstract
Cloud and cloud shadow detection is a crucial issue in remote sensing image processing. The backgrounds of clouds and cloud shadows are mostly complex in actual remote sensing images. Traditional methods are easily affected by ground object interference, noise interference and other factors, and problems such as missing detection and false detection are prone to occur in the process of cloud detection. In addition, due to insufficient edge information extraction capabilities, traditional methods have very rough segmentation results for cloud and cloud shadow boundaries. In order to improve the accuracy of cloud and cloud shadow detection, a Multi-scale Strip Pooling Feature Aggregation Network is proposed. This method uses the residual network as the backbone to extract different levels of semantic information. And, in order to improve the multi-scale information extraction ability of the network, an Improved Pyramid Pooling module is introduced to mine deep multi-scale semantic information. Then, the Mutual Fusion module is used to guide the fusion of different levels of information. Finally, in view of the problem of rough segmentation boundaries in traditional methods, the Strip Boundary Refinement module is used to repair the boundary information of clouds and cloud shadows. The experimental results conducted on the datasets collected by Landsat-8, Sentinel-2 and a public dataset HRC_WHU show that this method is superior to the existing methods.
Similar content being viewed by others
References
Zhang Y, Rossow WB, Lacis AA, Oinas V, Mishchenko MI (2004) Calculation of radiative fluxes from the surface to top of atmosphere based on isccp and other global data sets: refinements of the radiative transfer model and the input data. J Geophys Res Atmosp 109:D19105
Shen SS, Descour MR (2000) Algorithms for multispectral, hyperspectral, and ultraspectral imagery vi. Algorithms Multisp Hyperspectr Ultraspectr Imag 4:4049
Liu X, Jianmin X, Bingyu D (2005) A bi-channel dynamic thershold algorithm used in automatically identifying clouds on gms-5 imagery. J Appl Meteorl Sci 16(4):134–444
Ma F, Zhang Q, Guo N, Zhang J (2007) The study of cloud detection with multi-channel data of satellite. Chin J Atmosp Sci Chin Ed 31(1):119
Cao Q, Zheng H, Li X (2007) A method for detecting cloud in satellite remote sensing image based on texture. Acta Aeronaut Astronaut Sin 28(3):661
Molnar G, Coakley JA Jr (1985) Retrieval of cloud cover from satellite imagery data: a statistical approach. J Geophys Res Atmosp 90(D7):12960–12970
Liu Z, Li Y, Huang F (2007) Cloud detection of modis satellite images based on dynamical cluster. Remote Sens Inf 7:33–35
Corneliu Octavian Dumitru and Mihai Datcu (2013) Information content of very high resolution sar images: study of feature extraction and imaging parameters. IEEE Trans Geosci Remote Sens 51(8):4591–4610
Geng J, Fan J, Wang H, Ma X, Li B, Chen F (2015) High-resolution sar image classification via deep convolutional autoencoders. IEEE Geosci Remote Sens Lett 12(11):2351–2355
Liu M, Yan W, Zhao W, Zhang Q, Li M, Liao G (2013) Dempster-shafer fusion of multiple sparse representation and statistical property for sar target configuration recognition. IEEE Geosci Remote Sens Lett 11(6):1106–1110
Tian B, Shaikh MA, Azimi-Sadjadi MR, Haar THV, Reinke DL (1999) A study of cloud classification with neural networks using spectral and textural features. IEEE Trans Neural Netw 10(1):138–151
Gómez-Chova L, Amorós J, Camps-Valls G, Martin JD, Calpe J, Alonso L, Guanter L, Fortea JC, Moreno J (2005) Cloud detection for chris/proba hyperspectral images. In: Remote sensing of clouds and the atmosphere X, vol 5979. International Society for Optics and Photonics, pp 59791Q
Gómez-Chova L, Camps-Valls G, Amorós-López J, Guanter L, Alonso L, Calpe J, Moreno J et al (2006) New cloud detection algorithm for multispectral and hyperspectral images: application to envisat/meris and proba/chris sensors. In: IEEE international geoscience and remote sensing symposium. IGARSS, pp 2757–2760
Gao J, Wang K, Tian X, Chen J (2018) A bp-nn based cloud detection method for fy-4 remote sensing images. J Infrared Millim Waves 37:477–485
Zhang B, Yadong H, Hong J (2021) Cloud detection of remote sensing images based on h-svm with multi-feature fusion. J Atmosp Environ Opt 16(1):58
Xia M, Wang K, Song W, Chen C, Li Y et al (2020) Non-intrusive load disaggregation based on composite deep long short-term memory network. Expert Syst Appl 160:113669
Xia M, Cui Y, Zhang Y, Yiming X, Liu J, Yiqing X (2021) Dau-net: a novel water areas segmentation structure for remote sensing image. Int J Remote Sens 42(7):2594–2621
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on Medical image computing and computer-assisted intervention. Springer, pp 234–241
Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Lafferty J, McCallum A, Pereira FCN (2001) Probabilistic models for segmenting and labeling sequence data, conditional random fields. In: Proceedings of the eighteenth international conference on machine learning, pp 282–289
Zhao H, iShi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881–2890
Chen B, Xia M, Huang J (2021) Mfanet: a multi-level feature aggregation network for semantic segmentation of land cover. Remote Sens 13(4):731
Zhang L, Sheng Z, Li Y, Sun Q, Zhao Y, Feng D (2019) Image object detection and semantic segmentation based on convolutional neural network. Neural Computi Appl 32:1949–1958
Zhang Y, Li X, Lin M, Chiu B, Zhao M (2020) Deep-recursive residual network for image semantic segmentation. Neural Comput Appl 32(16):12935–12947
Zheng X, Chen T (2021) High spatial resolution remote sensing image segmentation based on the multiclassification model and the binary classification model. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05561-8
Xia M, Wang T, Zhang Y, Liu J, Yiqing X (2021) Cloud/shadow segmentation based on global attention feature fusion residual network for remote sensing imagery. Int J Remote Sens 42(6):2022–2045
Mohajerani S, Saeedi P (2019) Cloud-net: an end-to-end cloud detection algorithm for landsat 8 imagery. In: IGARSS 2019-2019 IEEE international geoscience and remote sensing symposium. IEEE, pp 1029–1032
Zhan Y, Wang J, Shi J, Cheng G, Yao L, Sun W (2017) Distinguishing cloud and snow in satellite images via deep convolutional network. IEEE Geosci Remote Sens Lett 14(10):1785–1789
Li J, Zhao P, Fang W, Song S (2020) Cloud detection of multi-angle remote sensing image based on deep learning. J Atmosp Environ Opt 15(05):380
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794–7803
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv:1706.03762
Hou Q, Zhang L, Cheng M, Feng J (2020) Strip pooling: rethinking spatial pooling for scene parsing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4003–4012
Hanchao L, Pengfei X, Jie A, Lingxue W (2018) Pyramid attention network for semantic segmentation. arXiv:1805.10180
Yu C, Gao C, Wang J, Yu G, Shen C, Sang N (2020) Bisenet v2: bilateral network with guided aggregation for real-time semantic segmentation. arXiv:2004.02147
Li Z, Shen H, Cheng Q, Liu Y, You S, He Z (2019) Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors. ISPRS J Photogramm Remote Sens 150:197–212
Acknowledgements
This work is supported by the National Natural Science Foundation of PR China (42075130).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lu, C., Xia, M. & Lin, H. Multi-scale strip pooling feature aggregation network for cloud and cloud shadow segmentation. Neural Comput & Applic 34, 6149–6162 (2022). https://doi.org/10.1007/s00521-021-06802-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-06802-0