{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:22:36Z","timestamp":1777389756030,"version":"3.51.4"},"reference-count":58,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T00:00:00Z","timestamp":1610409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen International S&amp;T Cooperation Project","award":["GJHZ20190821155805960"],"award-info":[{"award-number":["GJHZ20190821155805960"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971386"],"award-info":[{"award-number":["41971386"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801223"],"award-info":[{"award-number":["41801223"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the General Research Fund","award":["HKBU 12301820"],"award-info":[{"award-number":["HKBU 12301820"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations.<\/jats:p>","DOI":"10.3390\/rs13020236","type":"journal-article","created":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T20:11:31Z","timestamp":1610482291000},"page":"236","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A New Machine Learning Approach in Detecting the Oil Palm Plantations Using Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Kaibin","family":"Xu","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3279-6520","authenticated-orcid":false,"given":"Jing","family":"Qian","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4259-9141","authenticated-orcid":false,"given":"Zengyun","family":"Hu","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Research Center for Ecology and Environment of Central Asia, CAS, Urumqi 830011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0173-663X","authenticated-orcid":false,"given":"Zheng","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, S-223 62 Lund, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoliang","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Research Center for Ecology and Environment of Central Asia, CAS, Urumqi 830011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"TripleSAI Technology, Shenzhen 518109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayu","family":"Sun","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujie","family":"Wei","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuwei","family":"Xing","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.rser.2018.12.050","article-title":"Interdependencies and telecoupling of oil palm expansion at the expense of Indonesian rainforest","volume":"105","author":"Rulli","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1002\/lite.200900067","article-title":"Oil palm: Future prospects for yield and quality improvements","volume":"21","author":"Murphy","year":"2009","journal-title":"Lipid Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.tree.2008.06.012","article-title":"How will oil palm expansion affect biodiversity?","volume":"23","author":"Fitzherbert","year":"2008","journal-title":"Trends Ecol. Evol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.jclepro.2015.05.048","article-title":"Greenhouse gas emissions from land use change due to oil palm expansion in Thailand for biodiesel production","volume":"134","author":"Permpool","year":"2016","journal-title":"J. Clean. Prod."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7559","DOI":"10.1073\/pnas.1200452109","article-title":"Committed carbon emissions, deforestation, and community land conversion from oil palm plantation expansion in West Kalimantan, Indonesia","volume":"109","author":"Carlson","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1073\/pnas.1704728114","article-title":"Effect of oil palm sustainability certification on deforestation and fire in Indonesia","volume":"115","author":"Carlson","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"044029","DOI":"10.1088\/1748-9326\/6\/4\/044029","article-title":"High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon","volume":"6","author":"DeFries","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9749","DOI":"10.3390\/rs6109749","article-title":"Oil palm tree detection with high resolution multi-spectral satellite imagery","volume":"6","author":"Srestasathiern","year":"2014","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, W., Fu, H., Yu, L., and Cracknell, A. (2017). Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sens., 9.","DOI":"10.3390\/rs9010022"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, W., Dong, R., Fu, H., and Yu, L. (2019). Large-scale oil palm tree detection from high-resolution satellite images using two-stage convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11010011"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1016\/j.foreco.2011.07.008","article-title":"Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data","volume":"262","author":"Morel","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.3390\/rs70201206","article-title":"Mapping oil palm plantations in Cameroon using PALSAR 50-m orthorectified mosaic images","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"012043","DOI":"10.1088\/1757-899X\/705\/1\/012043","article-title":"Oil Palm Plantation Monitoring from Satellite Image","volume":"705","author":"Sum","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_14","first-page":"183","article-title":"Assessment of ALOS-2 PALSAR-2L-band and Sentinel-1 C-band SAR backscatter for discriminating between large-scale oil palm plantations and smallholdings on tropical peatlands","volume":"13","author":"Oon","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-48443-3","article-title":"Oil palm concessions in southern Myanmar consist mostly of unconverted forest","volume":"9","author":"Nomura","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"012064","DOI":"10.1088\/1755-1315\/169\/1\/012064","article-title":"Application of Sentinel-1 satellite to identify oil palm plantations in Balikpapan Bay","volume":"169","author":"Lazecky","year":"2018","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5431","DOI":"10.1080\/01431161.2016.1241448","article-title":"Oil palm mapping using Landsat and PALSAR: A case study in Malaysia","volume":"37","author":"Cheng","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Poortinga, A., Tenneson, K., Shapiro, A., Nquyen, Q., San Aung, K., Chishtie, F., and Saah, D. (2019). Mapping plantations in Myanmar by fusing landsat-8, sentinel-2 and sentinel-1 data along with systematic error quantification. Remote Sens., 11.","DOI":"10.3390\/rs11070831"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7312","DOI":"10.1080\/01431161.2019.1579944","article-title":"Discriminating between large-scale oil palm plantations and smallholdings on tropical peatlands using vegetation indices and supervised classification of LANDSAT-8","volume":"40","author":"Oon","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7459","DOI":"10.1080\/01431161.2019.1597311","article-title":"Mapping the spatial distribution and changes of oil palm land cover using an open source cloud-based mapping platform","volume":"40","author":"Shaharum","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Daliman, S., Rahman, S.A., Bakar, S.A., and Busu, I. (2014, January 14\u201316). Segmentation of oil palm area based on GLCM-SVM and NDVI. Proceedings of the 2014 IEEE Region 10 Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/TENCONSpring.2014.6863113"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1080\/01431161.2011.631949","article-title":"Evaluating the potential to monitor aboveground biomass in forest and oil palm in Sabah, Malaysia, for 2000\u20132008 with Landsat ETM+ and ALOS-PALSAR","volume":"33","author":"Morel","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","first-page":"012015","article-title":"Comparison of Optic Landsat-8 and SAR Sentinel-1 in Oil Palm Monitoring, Case Study: Asahan, North Sumatera, Indonesia","volume":"Volume 280","author":"Carolita","year":"2019","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8174","DOI":"10.1080\/01431161.2018.1479799","article-title":"Comparison of visual and automated oil palm mapping in Borneo","volume":"40","author":"Miettinen","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Descals, A., Szantoi, Z., Meijaard, E., Sutikno, H., Rindanata, G., and Wich, S. (2019). Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra. Remote Sens., 11.","DOI":"10.3390\/rs11212590"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4778","DOI":"10.1080\/01431161.2014.930201","article-title":"Support vector machine to map oil palm in a heterogeneous environment","volume":"35","author":"Nooni","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sitthi, A., Nagai, M., Dailey, M., and Ninsawat, S. (2016). Exploring land use and land cover of geotagged social-sensing images using naive bayes classifier. Sustainability, 8.","DOI":"10.3390\/su8090921"},{"key":"ref_28","first-page":"100287","article-title":"Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms","volume":"17","author":"Shaharum","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7500","DOI":"10.1080\/01431161.2019.1569282","article-title":"Young and mature oil palm tree detection and counting using convolutional neural network deep learning method","volume":"40","author":"Mubin","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.snb.2012.11.071","article-title":"Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar","volume":"177","author":"Liu","year":"2013","journal-title":"Sens. Actuator B-Chem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7424","DOI":"10.1080\/01431161.2013.822601","article-title":"Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia","volume":"34","author":"Tan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1012450327387","article-title":"Choosing multiple parameters for support vector machines","volume":"46","author":"Chapelle","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_35","unstructured":"Susanti, A. (2016). Oil Palm Expansion in Indonesia: Serving People, Planet and Profit?, Eburon Academic Publishers."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"Miettinen, J., Liew, S.C., and Kwoh, L.K. (2015, January 19\u201323). Usability of Sentinel-1 dual polarization C-band data for plantation detection in insular Southeast Asia. Proceedings of the 36th Asian Conference Remote Sensing, Manila, Philippines."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/10106049209354353","article-title":"Using spectral vegetation indices to estimate rangeland productivity","volume":"7","author":"Richardson","year":"1992","journal-title":"Geocarto Int."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1080\/01431160500168686","article-title":"An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data","volume":"26","author":"Tucker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7297","DOI":"10.1080\/01431161.2019.1584689","article-title":"Detection and characterization of oil palm plantations through MODIS EVI time series","volume":"40","author":"Boccardo","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_45","first-page":"012065","article-title":"Application of SAR data for oil palm tree discrimination","volume":"Volume 169","author":"Kee","year":"2018","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_47","first-page":"1","article-title":"Image texture feature extraction using GLCM approach","volume":"3","author":"Mohanaiah","year":"2013","journal-title":"Int. J. Sci. Res. publications."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TGRS.1986.289643","article-title":"Textural infornation in SAR images","volume":"2","author":"Ulaby","year":"1986","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"Chandrashekar","year":"2014","journal-title":"Comput. Electr. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/BF00117831","article-title":"Some properties of splitting criteria","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3389\/fnhum.2019.00052","article-title":"Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization","volume":"13","author":"Wang","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref_52","first-page":"219","article-title":"Detecting industrial oil palm plantations on Landsat images with Google Earth Engine","volume":"4","author":"Lee","year":"2016","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1111\/j.1365-2664.2006.01214.x","article-title":"Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS)","volume":"43","author":"Allouche","year":"2006","journal-title":"J. Appl. Ecol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/10485252.2017.1404598","article-title":"Multiple predicting K-fold cross-validation for model selection","volume":"30","author":"Jung","year":"2018","journal-title":"J. Nonparametr. Stat."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/01431161.2010.520345","article-title":"Separability of insular Southeast Asian woody plantation species in the 50 m resolution ALOS PALSAR mosaic product","volume":"2","author":"Miettinen","year":"2011","journal-title":"Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"181","DOI":"10.14257\/ijmue.2014.9.11.18","article-title":"Application of improved grid search algorithm on SVM for classification of tumor gene","volume":"9","author":"Wenwen","year":"2014","journal-title":"Int. J. Multimed. Ubiquitous Eng."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Karaboga, D., and Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. International Fuzzy Systems Association World Congress, Springer.","DOI":"10.1007\/978-3-540-72950-1_77"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","article-title":"Ant colony optimization","volume":"1","author":"Dorigo","year":"2006","journal-title":"IEEE Comput. Intell. Mag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/236\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:10:00Z","timestamp":1760159400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,12]]},"references-count":58,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020236"],"URL":"https:\/\/doi.org\/10.3390\/rs13020236","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,12]]}}}