{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T09:56:04Z","timestamp":1779011764663,"version":"3.51.4"},"reference-count":49,"publisher":"National Library of Serbia","issue":"4","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:p>In the shadow detection task, the shadow model is usually consistent with the approximate contour of ontology semantics, it is difficult to extract the features of land covered objects or ground pixels, and easy to be confused into foreground objects in gray scale. Therefore, we present to formulate and apply one new threshold segmentation method based on information fusion for object shadow detection in remote sensing images. Firstly, object shadow pixels are screened using intensity and chromaticity information in HSI color space. Secondly, the remote sensing image is carried out by principal component analysis (PCA) to obtain the first principal component. A new shadow index is constructed using the results obtained from HSI and the first principal component. Thirdly, based on the results of the above two information fusion, a threshold segmentation model is established using the improved threshold segmentation algorithm between the maximum and the minimum threshold segmentation algorithm, so as to obtain the final object shadow detection results. Finally, affluent experiments are conducted on the datasets collected from Google Earth. The results show that the proposed object shadow detection algorithm in remote sensing images can achieve better segmentation and detection (more than 95%) effect compared with state-of-the-art methods.<\/jats:p>","DOI":"10.2298\/csis231230023y","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T12:23:20Z","timestamp":1715689400000},"page":"1221-1241","source":"Crossref","is-referenced-by-count":5,"title":["Threshold segmentation based on information fusion for object shadow detection in remote sensing images"],"prefix":"10.2298","volume":"21","author":[{"given":"Shoulin","family":"Yin","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Harbin Engineering University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communications Engineering, Dalian Minzu University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Teng","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Harbin Engineering University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Rodriguez-Moreno F, Kren J, Zemek F, et al. Advantage of multispectral imaging with subcentimeter resolution in precision agriculture: generalization of training for supervised classification[J]. Precision Agriculture, 2017, 18: 615-634.","DOI":"10.1007\/s11119-016-9478-1"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Sishodia R P, Ray R L, Singh S K. Applications of remote sensing in precision agriculture: A review[J]. Remote Sensing, 2020, 12(19): 3136.","DOI":"10.3390\/rs12193136"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Almeida L P, Almar R, Bergsma E W J, et al. Deriving high spatial-resolution coastal topography from sub-meter satellite stereo imagery[J]. Remote Sensing, 2019, 11(5): 590.","DOI":"10.3390\/rs11050590"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Moortgat J, Li Z, Durand M, et al. Deep learning models for river classification at sub-meter resolutions from multispectral and panchromatic commercial satellite imagery[J]. Remote Sensing of Environment, 2022, 282: 113279.","DOI":"10.1016\/j.rse.2022.113279"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Wohlfeil J, Hirschmller H, Piltz B, et al. Fully automated generation of accurate digital surface models with sub-meter resolution from satellite imagery[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, 39: 75-80.","DOI":"10.5194\/isprsarchives-XXXIX-B3-75-2012"},{"key":"ref6","unstructured":"Kumar S, Kaur A. Algorithm for shadow detection in real-colour images[J]. International Journal on Computer Science and Engineering, 2010, 2(07): 2444-2446."},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Srikantha A, Sidib D. Ghost detection and removal for high dynamic range images: Recent advances[J]. Signal Processing: Image Communication, 2012, 27(6): 650-662.","DOI":"10.1016\/j.image.2012.02.001"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Zhang H, Sun K, Li W. Object-oriented shadow detection and removal from urban highresolution remote sensing images[J]. IEEE transactions on geoscience and remote sensing, 2014, 52(11): 6972-6982.","DOI":"10.1109\/TGRS.2014.2306233"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Abd-El Monsef H, Smith S E. A new approach for estimating mangrove canopy cover using Landsat 8 imagery[J]. Computers and Electronics in Agriculture, 2017, 135: 183-194.","DOI":"10.1016\/j.compag.2017.02.007"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Shi L, Fang J, Zhao Y. Automatic shadow detection in high-resolution multispectral remote sensing images[J]. Computers and Electrical Engineering, 2023, 105: 108557.","DOI":"10.1016\/j.compeleceng.2022.108557"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Wang X, Voytenko D, Holland D M. Accuracy evaluation of digital elevation models derived from Terrestrial Radar Interferometer over Helheim Glacier, Greenland[J]. Remote Sensing of Environment, 2022, 268: 112759.","DOI":"10.1016\/j.rse.2021.112759"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Ward G J, Wang T, Geisler-Moroder D, et al. Modeling specular transmission of complex fenestration systems with data-driven BSDFs[J]. Building and Environment, 2021, 196: 107774.","DOI":"10.1016\/j.buildenv.2021.107774"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Du H, Chen X, Xi J. An improved background segmentation algorithm for fringe projection profilometry based on Otsu method[J]. Optics Communications, 2019, 453: 124206.","DOI":"10.1016\/j.optcom.2019.06.044"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Fu H, Zhou T, Sun C. Object-based shadow index via illumination intensity from high resolution satellite images over urban areas[J]. Sensors, 2020, 20(4): 1077.","DOI":"10.3390\/s20041077"},{"key":"ref15","unstructured":"Hou S W, Sun W F, Zheng X S. Overview of cloud detection methods in remote sensing images[ J]. Space Electronic Technology, 2014, 11(3): 68-76."},{"key":"ref16","unstructured":"Liu Z, Yang J,WangW, et al. Cloud detection methods for remote sensing images: a survey[J]. Chinese Space Science and Technology, 2023, 43(1): 1."},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Mahajan S, Fataniya B. Cloud detection methodologies: Variants and developmentA review[J]. Complex & Intelligent Systems, 2020, 6: 251-261.","DOI":"10.1007\/s40747-019-00128-0"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Siddiq S, Kaur K, Dhir R. Automatic Detection of Cloudy and Non-Cloudy SAR Images Using Convolutional Neural Networks[C]\/\/2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC). IEEE, 2023: 350-355.","DOI":"10.1109\/ICSCCC58608.2023.10176417"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"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, 2011, 50(4): 1140-1154.","DOI":"10.1109\/TGRS.2011.2164087"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"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"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Yin S, Wang L, Wang Q, et al. M2F2-RCNN: Multi-functional faster RCNN based on multiscale feature fusion for region search in remote sensing images[J]. Computer Science and Information Systems, 2023 (00): 54-54.","DOI":"10.2298\/CSIS230315054Y"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Zhu Z, Woodcock C E. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change[J]. Remote Sensing of Environment, 2014, 152: 217-234.","DOI":"10.1016\/j.rse.2014.06.012"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Teng L, Qiao Y, Shafiq M, et al. FLPK-BiSeNet: Federated Learning Based on Priori Knowledge and Bilateral Segmentation Network for Image Edge Extraction[J]. IEEE Transactions on Network and Service Management, 2023.","DOI":"10.1109\/TNSM.2023.3273991"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Xie F, Shi M, Shi Z, et al. Multilevel cloud detection in remote sensing images based on deep learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3631-3640.","DOI":"10.1109\/JSTARS.2017.2686488"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Qiu S, Zhu Z, He B. Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4C8 and Sentinel-2 imagery[J]. Remote Sensing of Environment, 2019, 231: 111205.","DOI":"10.1016\/j.rse.2019.05.024"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Chang H, Fan X, Huo L, et al. Improving Cloud Detection in WFV Images Onboard Chinese GF-1\/6 Satellite[J]. Remote Sensing, 2023, 15(21): 5229.","DOI":"10.3390\/rs15215229"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Xu D, Li X, Zhao L, et al. Hyperspectral Remote Sensing Image Cloud Detection Based on Spectral Analysis and Dynamic Fractal Dimension[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101003.","DOI":"10.3788\/LOP56.101003"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Calin M A, Calin A C, Nicolae D N. Application of airborne and spaceborne hyperspectral imaging techniques for atmospheric research: Past, present, and future[J]. Applied Spectroscopy Reviews, 2021, 56(4): 289-323.","DOI":"10.1080\/05704928.2020.1774381"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Zhang H, Huang Q, Zhai H, et al. Multi-temporal cloud detection based on robust PCA for optical remote sensing imagery[J]. Computers and Electronics in Agriculture, 2021, 188: 106342.","DOI":"10.1016\/j.compag.2021.106342"},{"key":"ref30","unstructured":"Zhang Y, Yang C, Tao R, et al. Multi-temporal Cloud Detection Method for Qinghai-Tibet Plateau based with FY-4A Data[J]. Remote Sensing Technology and Application, 2020, 35(2): 389-398."},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Khoomboon S, Kasetkasem T, Rakwatin P. A land cover mapping algorithm for thin to medium cloud-covered remote sensing images using a level set method[J]. International Journal of Remote Sensing, 2022, 43(10): 3803-3842.","DOI":"10.1080\/01431161.2022.2106162"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Candra D S, Phinn S, Scarth P. Automated cloud and cloud-shadow masking for Landsat 8 using multitemporal images in a variety of environments[J]. Remote Sensing, 2019, 11(17): 2060.","DOI":"10.3390\/rs11172060"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Zhang X, Liu L, Chen X, et al. A novel multitemporal cloud and cloud shadow detection method using the integrated cloud Z-scores model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(1): 123-134.","DOI":"10.1109\/JSTARS.2018.2889150"},{"key":"ref34","unstructured":"Yin S. Object Detection Based on Deep Learning: A Brief Review[J]. IJLAI Transactions on Science and Engineering, 2023, 1(02): 1-6."},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Zhu X, Gao F, Liu D, et al. A modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat images[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 9(3): 521-525.","DOI":"10.1109\/LGRS.2011.2173290"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Liu M, Liu X, Li J, et al. Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm[J]. International journal of applied earth observation and geoinformation, 2014, 33: 192-202.","DOI":"10.1016\/j.jag.2014.05.009"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Huang S, Ding J, Liu B, et al. The capability of integrating optical and microwave data for detecting soil moisture in an oasis region[J]. Remote Sensing, 2020, 12(9): 1358.","DOI":"10.3390\/rs12091358"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Li T, Cheng X. Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 101: 102356.","DOI":"10.1016\/j.jag.2021.102356"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Ji S, Dai P, Lu M, et al. Simultaneous cloud detection and removal from bitemporal remote sensing images using cascade convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(1): 732-748.","DOI":"10.1109\/TGRS.2020.2994349"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Abdullah A S S, Abed M A, Al Barazanchi I. Improving face recognition by elman neural network using curvelet transform and HSI color space[J]. Periodicals of Engineering and Natural Sciences, 2019, 7(2): 430-437.","DOI":"10.21533\/pen.v7i2.485"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Yin S, Li H. Hot region selection based on selective search and modified fuzzy C-means in remote sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 5862-5871.","DOI":"10.1109\/JSTARS.2020.3025582"},{"key":"ref42","unstructured":"LI ZW, ZHENGW, FANG J, et al. Optimizing suitability area of underwater gravity matching navigation based on a new principal component weighted average normalization method[J]. Chinese Journal of Geophysics, 2019, 62(9): 3269-3278."},{"key":"ref43","unstructured":"Jia Y L, Zhang W, Meng L K. A study of selection method of NDWI segmentation threshold for GF1 image[J]. Remote Sensing for Land and Resources, 2019, 31(01): 95-100."},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Hu Q,WuW, Xia T, et al. Exploring the use of Google Earth imagery and object-based methods in land use\/cover mapping[J]. Remote Sensing, 2013, 5(11): 6026-6042.","DOI":"10.3390\/rs5116026"},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"Amani M, Ghorbanian A, Ahmadi S A, et al. Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 5326-5350.","DOI":"10.1109\/JSTARS.2020.3021052"},{"key":"ref46","doi-asserted-by":"crossref","unstructured":"Ding J, Xue N, Xia G S, et al. Object detection in aerial images: A large-scale benchmark and challenges[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 44(11): 7778-7796.","DOI":"10.1109\/TPAMI.2021.3117983"},{"key":"ref47","doi-asserted-by":"crossref","unstructured":"Li D, Wang S, Xiang S, et al. Dual-stream shadow detection network: biologically inspired shadow detection for remote sensing images[J]. Neural Computing and Applications, 2022, 34(12): 10039-10049.","DOI":"10.1007\/s00521-022-06989-w"},{"key":"ref48","doi-asserted-by":"crossref","unstructured":"Liu D, Zhang J,Wu Y, et al. A shadow detection algorithm based on multiscale spatial attention mechanism for aerial remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.","DOI":"10.1109\/LGRS.2021.3100294"},{"key":"ref49","doi-asserted-by":"crossref","unstructured":"Alvarado-Robles G, Osornio-Rios R A, Solis-Munoz F J, et al. An approach for shadow detection in aerial images based on multi-channel statistics[J]. IEEE Access, 2021, 9: 34240-34250.","DOI":"10.1109\/ACCESS.2021.3061102"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T08:18:22Z","timestamp":1730276302000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142400023Y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.2298\/csis231230023y","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}