{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:49:39Z","timestamp":1776181779115,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T00:00:00Z","timestamp":1602028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Pre-Research Project of the \u201cThirteenth Five-Year-Plan\u201d of China","award":["305020903"],"award-info":[{"award-number":["305020903"]}]},{"name":"the Natural Science  Foundation of China","award":["61801359"],"award-info":[{"award-number":["61801359"]}]},{"name":"the Natural Science  Foundation of China","award":["61571345"],"award-info":[{"award-number":["61571345"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cloud detection, as a crucial step, has always been a hot topic in the field of optical remote sensing image processing. In this paper, we propose a deep learning cloud detection Network that is based on the Gabor transform and Attention modules with Dark channel subnet (NGAD). This network is based on the encoder-decoder framework. The information on texture is an important feature that is often used in traditional cloud detection methods. The NGAD enhances the attention of the network towards important texture features in the remote sensing images through the proposed Gabor feature extraction module. The channel attention module that is based on the larger scale features and spatial attention module that is based on the dark channel subnet have been introduced in NGAD. The channel attention module highlights the important information in a feature map from the channel dimensions, weakens the useless information, and helps the network to filter this information. A dark channel subnet with spatial attention module has been designed in order to further reduce the influence of the redundant information in the extracted features. By introducing a \u201cdark channel\u201d, the information in the feature map is reconstructed from the spatial dimension. The NGAD is validated while using the Gaofen-1 WFV imagery in four spectral bands. The experimental results show that the overall accuracy of NGAD reaches 97.42% and the false alarm rate reaches 2.22%. The efficiency of cloud detection using NGAD exceeds the state-of-art image segmentation network model and remote sensing image cloud detection model.<\/jats:p>","DOI":"10.3390\/rs12193261","type":"journal-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T08:52:41Z","timestamp":1602147161000},"page":"3261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Cloud Detection Method Using Convolutional Neural Network Based on Gabor Transform and Attention Mechanism with Dark Channel Subnet for Remote Sensing Image"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8495-2804","authenticated-orcid":false,"given":"Jing","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Qin","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Yuchen","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Yunsong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Yuzhou","family":"Chai","sequence":"additional","affiliation":[{"name":"Data Transmission Institute, China Academy of Space Technology, Xi'an 710000, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Data Transmission Institute, China Academy of Space Technology, Xi'an 710000, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6195","DOI":"10.1109\/TGRS.2019.2904868","article-title":"Cdnet: Cnn-based cloud detection for remote sensing imagery","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_2","first-page":"259","article-title":"Remote sensing and GIS for natural hazards assessment and disaster risk management","volume":"3","year":"2013","journal-title":"Treatise Geomorphol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1007\/s11069-012-0450-8","article-title":"Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques","volume":"65","author":"Adab","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shi, T., Xu, Q., Zou, Z., and Shi, Z. (2018). Automatic raft labeling for remote sensing images via dual-scale homogeneous convolutional neural network. Remote Sens., 10.","DOI":"10.3390\/rs10071130"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shi, Y., Qi, Z., Liu, X., Niu, N., and Zhang, H. (2019). Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data. Remote Sens., 11.","DOI":"10.3390\/rs11222719"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"761","DOI":"10.2151\/jmsj.2004.761","article-title":"Spatial distribution and seasonal variation of cloud over China based on ISCCP data and surface observations","volume":"82","author":"Li","year":"2004","journal-title":"J. Meteorol. Soc. Jpn. Ser. II"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1080\/01431169608949110","article-title":"Cloud detection from thermal infrared images using a segmentation technique","volume":"17","author":"Shin","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.solener.2012.11.015","article-title":"Equipment and methodologies for cloud detection and classification: A review","volume":"95","author":"Tapakis","year":"2013","journal-title":"Sol. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4167","DOI":"10.1016\/j.rse.2008.06.010","article-title":"Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America","volume":"112","author":"Luo","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1109\/TGRS.2008.916208","article-title":"Spatial and temporal varying thresholds for cloud detection in GOES imagery","volume":"46","author":"Jedlovec","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xiong, Q., Wang, Y., Liu, D., Ye, S., Du, Z., Liu, W., Huang, J., Su, W., Zhu, D., and Yao, X. (2020). A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12030450"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bai, T., Li, D., Sun, K., Chen, Y., and Li, W. (2016). Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion. Remote Sens., 8.","DOI":"10.3390\/rs8090715"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s41651-019-0037-y","article-title":"Cloud detection in high-resolution remote sensing images using multi-features of ground objects","volume":"3","author":"Zhang","year":"2019","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/LGRS.2017.2735801","article-title":"Distinguishing cloud and snow in satellite images via deep convolutional network","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.amc.2008.05.050","article-title":"Automatic cloud removal from multi-temporal SPOT images","volume":"205","author":"Tseng","year":"2008","journal-title":"Appl. Math. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.rse.2010.03.002","article-title":"A multi-temporal method for cloud detection, applied to FORMOSAT-2, VEN\u03bcS, LANDSAT and SENTINEL-2 images","volume":"114","author":"Hagolle","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mateo-Garc\u00eda, G., G\u00f3mez-Chova, L., and Camps-Valls, G. (2017, January 23\u201328). Convolutional neural networks for multispectral image cloud masking. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127438"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TGRS.2019.2935177","article-title":"Gated and Axis-Concentrated Localization Network for Remote Sensing Object Detection","volume":"58","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, Q., Mou, L., Jiang, K., Liu, Q., Wang, Y., and Zhu, X.X. (2018, January 22\u201327). Hierarchical region based convolution neural network for multiscale object detection in remote sensing images. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518345"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2612","DOI":"10.1109\/JSTARS.2019.2906387","article-title":"Densely based multi-scale and multi-modal fully convolutional networks for high-resolution remote-sensing image semantic segmentation","volume":"12","author":"Peng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","unstructured":"Mou, L., and Zhu, X.X. (2018). RiFCN: Recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1109\/JSTARS.2017.2686488","article-title":"Multilevel cloud detection in remote sensing images based on deep learning","volume":"10","author":"Xie","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zi, Y., Xie, F., and Jiang, Z. (2018). A cloud detection method for Landsat 8 images based on PCANet. Remote Sens., 10.","DOI":"10.3390\/rs10060877"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2019.03.039","article-title":"A cloud detection algorithm for satellite imagery based on deep learning","volume":"229","author":"Jeppesen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112045","DOI":"10.1016\/j.rse.2020.112045","article-title":"Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning","volume":"250","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yu, J., Li, Y., Zheng, X., Zhong, Y., and He, P. (2020). An Effective Cloud Detection Method for Gaofen-5 Images via Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12132106"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/LGRS.2018.2878239","article-title":"Cloud detection in satellite images based on natural scene statistics and Gabor features","volume":"16","author":"Deng","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","unstructured":"Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014, January 8\u201313). Recurrent models of visual attention. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1109\/TMI.2019.2927226","article-title":"A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection","volume":"39","author":"Li","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_35","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015, January 9\u201311). Show, attend and tell: Neural image caption generation with visual attention. Proceedings of the International Conference on Machine Learning, Miami, FL, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, J., Feng, L., Pang, X., Gong, W., and Zhao, X. (2016). Radiometric cross calibration of gaofen-1 wfv cameras using landsat-8 oli images: A simple image-based method. Remote Sens., 8.","DOI":"10.3390\/rs8050411"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.rse.2017.01.026","article-title":"Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery","volume":"191","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, X., and Shi, Z. (2018). Utilizing multilevel features for cloud detection on satellite imagery. Remote Sens., 10.","DOI":"10.3390\/rs10111853"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2019.02.017","article-title":"Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors","volume":"150","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_41","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_42","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chethan, H., Kumar, G.H., and Raghavendra, R. (2009, January 27\u201328). Texture based approach for cloud classification using SVM. Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, India.","DOI":"10.1109\/ARTCom.2009.43"},{"key":"ref_44","unstructured":"Bhate, D., Chan, D., and Subbarayan, G. (2006, January 24\u201326). Non-empirical modeling of fatigue in lead-free solder joints: Fatigue failure analysis and estimation of fracture parameters. Proceedings of the EuroSime 2006\u20147th International Conference on Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems, Como, Italy."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, P., Zhong, W., Yang, Z., and Yang, F. (2020). JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification. Remote Sens., 12.","DOI":"10.3390\/rs12122016"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1016\/j.patcog.2009.01.001","article-title":"Automatic 3D face recognition from depth and intensity Gabor features","volume":"42","author":"Xu","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"14806","DOI":"10.3390\/rs71114806","article-title":"Weighted-fusion-based representation classifiers for hyperspectral imagery","volume":"7","author":"Peng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TIP.2018.2865280","article-title":"Attention couplenet: Fully convolutional attention coupling network for object detection","volume":"28","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yu, Y., Choi, J., Kim, Y., Yoo, K., Lee, S.H., and Kim, G. (2017, January 21\u201326). Supervising neural attention models for video captioning by human gaze data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.648"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.3390\/rs6064907","article-title":"Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing","volume":"6","author":"Hughes","year":"2014","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7172","DOI":"10.1002\/2015JD024722","article-title":"A universal dynamic threshold cloud detection algorithm (UDTCDA) supported by a prior surface reflectance database","volume":"121","author":"Sun","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7264","DOI":"10.1109\/TGRS.2014.2310240","article-title":"Cloud detection of RGB color aerial photographs by progressive refinement scheme","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4206","DOI":"10.1109\/JSTARS.2015.2438015","article-title":"Scene learning for cloud detection on remote-sensing images","volume":"8","author":"An","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:17:14Z","timestamp":1760177834000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,7]]},"references-count":54,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193261"],"URL":"https:\/\/doi.org\/10.3390\/rs12193261","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,7]]}}}