{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T02:24:59Z","timestamp":1777083899638,"version":"3.51.4"},"reference-count":73,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Program of Humanities and Social Sciences Foundation, Ministry of Education of China","award":["18YJCZH093"],"award-info":[{"award-number":["18YJCZH093"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M632565"],"award-info":[{"award-number":["2018M632565"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Channel Post-Doctoral Exchange Funding Scheme","award":["none"],"award-info":[{"award-number":["none"]}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2021J01128"],"award-info":[{"award-number":["2021J01128"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach\u2019s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.<\/jats:p>","DOI":"10.3390\/s22020551","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:33:04Z","timestamp":1641933184000},"page":"551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9114-8152","authenticated-orcid":false,"given":"Chih-Wei","family":"Lin","sequence":"first","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Forestry Post-Doctoral Station, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Fujian Universities for Ecology and Resource Statistics, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Cross-Strait Nature Reserve Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuping","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengxiang","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sidi","family":"Hong","sequence":"additional","affiliation":[{"name":"College of New Engineering Industry, Putian University, Putian 351100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1175\/JHM583.1","article-title":"A gauge-based analysis of daily precipitation over East Asia","volume":"8","author":"Xie","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, M., Shi, W., Xie, P., Silva, V.B., Kousky, V.E., Wayne Higgins, R., and Janowiak, J.E. (2008). Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res. Atmos., 113.","DOI":"10.1029\/2007JD009132"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1175\/1520-0477(1979)060<1048:RMORS>2.0.CO;2","article-title":"Radar measurement of rainfall\u2014A summary","volume":"60","author":"Wilson","year":"1979","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1256\/qj.05.190","article-title":"Radar precipitation measurement in a mountainous region","volume":"132","author":"Germann","year":"2006","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1175\/BAMS-88-1-47","article-title":"Comparison of near-real-time precipitation estimates from satellite observations and numerical models","volume":"88","author":"Ebert","year":"2007","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tian, Y., Peters-Lidard, C.D., Eylander, J.B., Joyce, R.J., Huffman, G.J., Adler, R.F., Hsu, K.l., Turk, F.J., Garcia, M., and Zeng, J. (2009). Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2009JD011949"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5817","DOI":"10.1007\/s11042-015-2520-x","article-title":"Vehicle detection and recognition for intelligent traffic surveillance system","volume":"76","author":"Tang","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijmedinf.2016.04.007","article-title":"Smart homes and home health monitoring technologies for older adults: A systematic review","volume":"91","author":"Liu","year":"2016","journal-title":"Int. J. Med. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3603","DOI":"10.1109\/TVT.2011.2162862","article-title":"Real-time security monitoring around a video surveillance vehicle with a pair of two-camera omni-imaging devices","volume":"60","author":"Yuan","year":"2011","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., and Yan, S. (2017, January 21\u201326). Deep joint rain detection and removal from a single image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.183"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TIP.2017.2691802","article-title":"Clearing the skies: A deep network architecture for single-image rain removal","volume":"26","author":"Fu","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.atmosres.2016.04.016","article-title":"Assessment of measurement errors and dynamic calibration methods for three different tipping bucket rain gauges","volume":"178","author":"Shedekar","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.2166\/nh.1986.0013","article-title":"The dynamic calibration of tipping-bucket raingauges","volume":"17","author":"Niemczynowicz","year":"1986","journal-title":"Hydrol. Res."},{"key":"ref_14","first-page":"200","article-title":"Design of a high precision weighing rain cauge based on WSN","volume":"33","author":"Tang","year":"2014","journal-title":"Meas. Control Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1127\/metz\/2019\/0934","article-title":"Detecting temperature induced spurious precipitation in a weighing rain gauge","volume":"28","author":"Knechtl","year":"2019","journal-title":"Meteorol. Z."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1007\/s12517-016-2425-7","article-title":"Inconsistency in rainfall characteristics estimated from records of different rain gauges","volume":"9","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/S0022-1694(99)00092-X","article-title":"Optimal areal rainfall estimation using raingauges and satellite data","volume":"222","author":"Grimes","year":"1999","journal-title":"J. Hydrol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.gloplacha.2005.12.004","article-title":"Stable water isotopes in the atmosphere\/biosphere\/lithosphere interface: Scaling-up from the local to continental scale, under humid and dry conditions","volume":"51","author":"Gat","year":"2006","journal-title":"Glob. Planet. Chang."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.5194\/hess-24-2287-2020","article-title":"Conditional simulation of surface rainfall fields using modified phase annealing","volume":"24","author":"Yan","year":"2020","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Suseno, D.P.Y., and Yamada, T.J. (2020). Simulating flash floods using geostationary satellite-based rainfall estimation coupled with a land surface model. Hydrology, 7.","DOI":"10.3390\/hydrology7010009"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sevruk, B. (1997). Regional dependency of precipitation-altitude relationship in the Swiss Alps. Climatic Change at High Elevation Sites, Springer.","DOI":"10.1007\/978-94-015-8905-5_7"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1175\/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2","article-title":"A technique for maximizing details in numerical weather map analysis","volume":"3","author":"Barnes","year":"1964","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1016\/j.jhydrol.2018.05.027","article-title":"Spatial interpolation of precipitation from multiple rain gauge networks and weather radar data for operational applications in Alpine catchments","volume":"563","author":"Foehn","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_24","unstructured":"Ryzhkov, A., and Zrnic, D. (2005, January 22\u201329). Radar polarimetry at S, C, and X bands: Comparative analysis and operational implications. Proceedings of the 32nd Conference on Radar Meteorology, Norman, OK, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"124248","DOI":"10.1016\/j.jhydrol.2019.124248","article-title":"Optimized raindrop size distribution retrieval and quantitative rainfall estimation from polarimetric radar","volume":"580","author":"Huang","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1175\/1520-0450(2001)040<2115:REFACO>2.0.CO;2","article-title":"Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network","volume":"39","author":"Bellerby","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1175\/2009JHM1077.1","article-title":"Evaluating the utility of multispectral information in delineating the areal extent of precipitation","volume":"10","author":"Behrangi","year":"2009","journal-title":"J. Hydrometeorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1175\/JAM2173.1","article-title":"Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system","volume":"43","author":"Hong","year":"2004","journal-title":"J. Appl. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1109\/36.536538","article-title":"A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors","volume":"34","author":"Kummerow","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"124705","DOI":"10.1016\/j.jhydrol.2020.124705","article-title":"Improving satellite rainfall estimation from MSG data in Northern Algeria by using a multi-classifier model based on machine learning","volume":"584","author":"Lazri","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.atmosres.2018.05.001","article-title":"Improvement of rainfall estimation from MSG data using Random Forests classification and regression","volume":"211","author":"Ouallouche","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1175\/JHM-D-17-0077.1","article-title":"A two-stage deep neural network framework for precipitation estimation from bispectral satellite information","volume":"19","author":"Tao","year":"2018","journal-title":"J. Hydrometeorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8612","DOI":"10.1109\/TGRS.2020.2989183","article-title":"Infrared precipitation estimation using convolutional neural network","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1007\/s11263-011-0421-7","article-title":"Rain or snow detection in image sequences through use of a histogram of orientation of streaks","volume":"93","author":"Bossu","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1109\/TIP.2011.2179057","article-title":"Automatic single-image-based rain streaks removal via image decomposition","volume":"21","author":"Kang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1109\/TIP.2015.2428933","article-title":"Video deraining and desnowing using temporal correlation and low-rank matrix completion","volume":"24","author":"Kim","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","first-page":"1981","article-title":"Estimation of rain drop analysis using image processing","volume":"4","author":"Sawant","year":"2015","journal-title":"Int. J. Sci. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10276","DOI":"10.1109\/TGRS.2019.2933054","article-title":"Automatic Precipitation Measurement Based on Raindrop Imaging and Artificial Intelligence","volume":"57","author":"Hsieh","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Roser, M., and Moosmann, F. (2008, January 4\u20136). Classification of weather situations on single color images. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621205"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4008","DOI":"10.1109\/TPAMI.2020.2997456","article-title":"Refineface: Refinement neural network for high performance face detection","volume":"43","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4204","DOI":"10.1109\/TVT.2019.2895651","article-title":"Dual cross-entropy loss for small-sample fine-grained vehicle classification","volume":"68","author":"Li","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101113","DOI":"10.1016\/j.ecoinf.2020.101113","article-title":"A pipeline for identification of bird and frog species in tropical soundscape recordings using a convolutional neural network","volume":"59","author":"LeBien","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hossain, M., Rekabdar, B., Louis, S.J., and Dascalu, S. (2015, January 12\u201317). Forecasting the weather of Nevada: A deep learning approach. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280812"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1175\/JHM-D-19-0110.1","article-title":"PERSIANN-CNN: Precipitation estimation from remotely sensed information using artificial neural networks\u2013convolutional neural networks","volume":"20","author":"Sadeghi","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Sun, J., and Tang, X. (2010, January 5\u201311). Guided image filtering. Proceedings of the European Conference on Computer Vision, Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-642-15549-9_1"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, D., Zhu, Y., Tian, L., and Shan, Y. (2020, January 13\u201319). Dual super-resolution learning for semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00383"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"113455","DOI":"10.1016\/j.eswa.2020.113455","article-title":"Flower classification with modified multimodal convolutional neural networks","volume":"159","author":"Bae","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_49","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, W., Liu, Z., Yu, Z., Dai, B., Lin, R., Wang, Y., Rehg, J.M., and Song, L. (2018, January 18\u201322). Decoupled Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00293"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., and Wang, L. (2018, January 8\u201314). Learning to navigate for fine-grained classification. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_26"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, Y., Bai, Y., Zhang, W., and Mei, T. (2019, January 15\u201320). Destruction and construction learning for fine-grained image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00530"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high-resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_58","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_59","first-page":"80","article-title":"Rethinking the Role of Activation Functions in Deep Convolutional Neural Networks for Image Classification","volume":"28","author":"Zheng","year":"2020","journal-title":"Eng. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.jvcir.2016.11.003","article-title":"Understanding convolutional neural networks with a mathematical model","volume":"41","author":"Kuo","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Carvalho, D.V., Pereira, E.M., and Cardoso, J.S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8.","DOI":"10.3390\/electronics8080832"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1145\/3359786","article-title":"Techniques for interpretable machine learning","volume":"63","author":"Du","year":"2019","journal-title":"Commun. ACM"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Buhrmester, V., M\u00fcnch, D., and Arens, M. (2021). Analysis of explainers of black box deep neural networks for computer vision: A survey. Mach. Learn. Knowl. Extr., 3.","DOI":"10.3390\/make3040048"},{"key":"ref_65","unstructured":"Xie, N., Ras, G., van Gerven, M., and Doran, D. (2020). Explainable deep learning: A field guide for the uninitiated. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1016\/j.ymssp.2005.07.002","article-title":"A new envelope algorithm of Hilbert\u2013Huang transform","volume":"20","author":"Qin","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1002\/joc.1435","article-title":"Understanding rainfall spatial variability in southeast USA at different timescales","volume":"27","author":"Baigorria","year":"2007","journal-title":"Int. J. Climatol. A J. R. Meteorol. Soc."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s12517-016-2392-z","article-title":"Evaluation of rainfall spatial correlation effect on rainfall-runoff modeling uncertainty, considering 2-copula","volume":"9","author":"Razmkhah","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"200","DOI":"10.12677\/JISP.2018.74023","article-title":"3D convolutional neural network for regional precipitation nowcasting","volume":"7","author":"Wu","year":"2018","journal-title":"J. Image Signal Process."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.5194\/gmd-13-2631-2020","article-title":"RainNet v1. 0: A convolutional neural network for radar-based precipitation nowcasting","volume":"13","author":"Ayzel","year":"2020","journal-title":"Geosci. Model Dev."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:35:22Z","timestamp":1760366122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,11]]},"references-count":73,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020551"],"URL":"https:\/\/doi.org\/10.3390\/s22020551","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,11]]}}}