{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T21:31:26Z","timestamp":1769203886379,"version":"3.49.0"},"reference-count":83,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T00:00:00Z","timestamp":1609804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["647038 BIODESERT"],"award-info":[{"award-number":["647038 BIODESERT"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European LIFE Project ADAPTAMED LIFE14","award":["CCA\/ES\/000612"],"award-info":[{"award-number":["CCA\/ES\/000612"]}]},{"name":"RH2O-ARID funded by Consejer\u00eda de Econom\u00eda, Conocimiento, Empresas y Universidad from the Junta de Andaluc\u00eda and the European Union Funds for Regional Development","award":["P18-RT-5130"],"award-info":[{"award-number":["P18-RT-5130"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.<\/jats:p>","DOI":"10.3390\/s21010320","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T21:18:57Z","timestamp":1609881537000},"page":"320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5348-7391","authenticated-orcid":false,"given":"Emilio","family":"Guirado","sequence":"first","affiliation":[{"name":"Multidisciplinary Institute for Environment Studies \u201cRamon Margalef\u201d University of Alicante, Edificio Nuevos Institutos, Carretera de San Vicente del Raspeig s\/n San Vicente del Raspeig, 03690 Alicante, Spain"},{"name":"Andalusian Center for Assessment and monitoring of global change (CAESCG), University of Almeria, 04120 Almeria, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1244-5704","authenticated-orcid":false,"given":"Javier","family":"Blanco-Sacrist\u00e1n","sequence":"additional","affiliation":[{"name":"College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Cornwall TR10 9EZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5934-3214","authenticated-orcid":false,"given":"Emilio","family":"Rodr\u00edguez-Caballero","sequence":"additional","affiliation":[{"name":"Agronomy Department, University of Almeria, 04120 Almeria, Spain"},{"name":"Centro de Investigaci\u00f3n de Colecciones Cient\u00edficas de la Universidad de Almer\u00eda (CECOUAL), 04120 Almeria, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4093-5356","authenticated-orcid":false,"given":"Siham","family":"Tabik","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8988-4540","authenticated-orcid":false,"given":"Domingo","family":"Alcaraz-Segura","sequence":"additional","affiliation":[{"name":"Department of Botany, Faculty of Science, University of Granada, 18071 Granada, Spain"},{"name":"iEcolab, Inter-University Institute for Earth System Research, University of Granada, 18006 Granada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5859-5674","authenticated-orcid":false,"given":"Jaime","family":"Mart\u00ednez-Valderrama","sequence":"additional","affiliation":[{"name":"Multidisciplinary Institute for Environment Studies \u201cRamon Margalef\u201d University of Alicante, Edificio Nuevos Institutos, Carretera de San Vicente del Raspeig s\/n San Vicente del Raspeig, 03690 Alicante, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5123-964X","authenticated-orcid":false,"given":"Javier","family":"Cabello","sequence":"additional","affiliation":[{"name":"Andalusian Center for Assessment and monitoring of global change (CAESCG), University of Almeria, 04120 Almeria, Spain"},{"name":"Department of Biology and Geology, University of Almeria, 04120 Almeria, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.scitotenv.2018.11.215","article-title":"Dryland changes under different levels of global warming","volume":"655","author":"Koutroulis","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1002\/esp.1181","article-title":"The role of vegetation patterns in structuring runoff and sediment fluxes in drylands","volume":"30","year":"2005","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.geomorph.2009.11.023","article-title":"Land degradation in drylands: Interactions among hydrologic\u2013aeolian erosion and vegetation dynamics","volume":"116","author":"Ravi","year":"2010","journal-title":"Geomorphology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gao, Z., Sun, B., Li, Z., Del Barrio, G., and Li, X. (2016, January 10\u201315). Desertification monitoring and assessment: A new remote sensing method. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729988"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Guirado, E., Blanco-Sacrist\u00e1n, J., Rigol-S\u00e1nchez, J., Alcaraz-Segura, D., and Cabello, J. (2019). A Multi-Temporal Object-Based Image Analysis to Detect Long-Lived Shrub Cover Changes in Drylands. Remote Sens., 11.","DOI":"10.3390\/rs11222649"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e1933","DOI":"10.1002\/eco.1933","article-title":"Remote-sensing-derived fractures and shrub patterns to identify groundwater dependence","volume":"11","author":"Guirado","year":"2018","journal-title":"Ecohydrology"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Guirado, E., Tabik, S., Alcaraz-Segura, D., Cabello, J., and Herrera, F. (2017). Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study. Remote Sens., 9.","DOI":"10.3390\/rs9121220"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"K\u00e9fi, S., Guttal, V., Brock, W.A., Carpenter, S.R., Ellison, A.M., Livina, V.N., Seekell, D.A., Scheffer, M., van Nes, E.H., and Dakos, V. (2014). Early warning signals of ecological transitions: Methods for spatial patterns. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0092097"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2016.05.019","article-title":"Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry","volume":"183","author":"Cunliffe","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2016.05.027","article-title":"Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics","volume":"183","author":"Brandt","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1038\/s41559-017-0382-5","article-title":"Author Correction: Plant spatial patterns identify alternative ecosystem multifunctionality states in global drylands","volume":"2","author":"Berdugo","year":"2018","journal-title":"Nat. Ecol. Evol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2016.12.003","article-title":"Application of time series of remotely sensed normalized difference water, vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone floodplains","volume":"190","author":"Mohammadi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.rse.2016.02.056","article-title":"Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel","volume":"177","author":"Tian","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111467","DOI":"10.1016\/j.rse.2019.111467","article-title":"Spectral vegetation indices of wetland greenness: Responses to vegetation structure, composition, and spatial distribution","volume":"234","author":"Taddeo","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_17","unstructured":"Zhang, J., and Jia, L. (2014, January 11\u201314). A comparison of pixel-based and object-based land cover classification methods in an arid\/semi-arid environment of Northwestern China. Proceedings of the 2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Changsha, China."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Amitrano, D., Guida, R., and Iervolino, P. (August, January 28). High Level Semantic Land Cover Classification of Multitemporal Sar Images Using Synergic Pixel-Based and Object-Based Methods. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899109"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, S., Yan, M., and Xu, J. (2020, January 28\u201331). Garbage object recognition and classification based on Mask Scoring RCNN. Proceedings of the 2020 International Conference on Culture-oriented Science & Technology (ICCST), Beijing, China.","DOI":"10.1109\/ICCST50977.2020.00016"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6997","DOI":"10.1109\/ACCESS.2020.2964055","article-title":"Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.inffus.2016.03.003","article-title":"A review of remote sensing image fusion methods","volume":"32","author":"Ghassemian","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Belgiu, M., and Stein, A. (2019). Spatiotemporal Image Fusion in Remote Sensing. Remote Sens., 11.","DOI":"10.3390\/rs11070818"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111901","DOI":"10.1016\/j.rse.2020.111901","article-title":"Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud","volume":"247","author":"Maneta","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10661-015-5089-y","article-title":"Monitoring changes in landscape pattern: Use of Ikonos and Quickbird images","volume":"188","author":"Alphan","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Granger, J.E., Mohammadimanesh, F., Warren, S., Puestow, T., Salehi, B., and Brisco, B. (2020). Smart solutions for smart cities: Urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John\u2019s, NL, Canada. J. Environ. Manag., 111676. In press.","DOI":"10.1016\/j.jenvman.2020.111676"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s10661-020-08612-8","article-title":"Detecting the development stages of natural forests in northern Iran with different algorithms and high-resolution data from GeoEye-1","volume":"192","author":"Mataji","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fawcett, D., Bennie, J., and Anderson, K. (2020). Monitoring spring phenology of individual tree crowns using drone\u2014Acquired NDVI data. Remote Sens. Ecol. Conserv.","DOI":"10.1002\/rse2.184"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6026","DOI":"10.3390\/rs5116026","article-title":"Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use\/Cover Mapping","volume":"5","author":"Hu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Venkatappa, M., Sasaki, N., Shrestha, R.P., Tripathi, N.K., and Ma, H.O. (2019). Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform. Remote Sens., 11.","DOI":"10.3390\/rs11131514"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sowmya, D.R., Deepa Shenoy, P., and Venugopal, K.R. (2019, January 29\u201331). Feature-based Land Use\/Land Cover Classification of Google Earth Imagery. Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India.","DOI":"10.1109\/I2CT45611.2019.9033586"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111953","DOI":"10.1016\/j.rse.2020.111953","article-title":"Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem","volume":"247","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-020-08522-9","article-title":"Assessment of volunteered geographic information for vegetation mapping","volume":"192","author":"Uyeda","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1002\/rse2.127","article-title":"Drones as a tool to monitor human impacts and vegetation changes in parks and protected areas","volume":"6","author":"Munoz","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"799","DOI":"10.14358\/PERS.72.7.799","article-title":"Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery","volume":"72","author":"Yu","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"661","DOI":"10.14358\/PERS.76.6.661","article-title":"Acquisition, Orthorectification, and Object-based Classification of Unmanned Aerial Vehicle (UAV) Imagery for Rangeland Monitoring","volume":"76","author":"Laliberte","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_36","first-page":"884","article-title":"Comparing object-based and pixel-based classifications for mapping savannas","volume":"13","author":"Whiteside","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.isprsjprs.2013.05.003","article-title":"Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective","volume":"82","author":"Arvor","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Johnson, B.A., and Ma, L. (2020). Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers\u2019 Views on the Future Priorities. Remote Sens., 12.","DOI":"10.3390\/rs12111772"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A review of semantic segmentation using deep neural networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Singh, R., and Rani, R. (2020). Semantic Segmentation using Deep Convolutional Neural Network: A Review. SSRN Electron. J.","DOI":"10.2139\/ssrn.3565919"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"145","DOI":"10.5194\/isprs-archives-XLI-B7-145-2016","article-title":"Assessment of multiresolution segmentation for extracting greenhouses from worldview-2 imagery","volume":"XLI-B7","author":"Aguilar","year":"2016","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/s41586-020-2824-5","article-title":"An unexpectedly large count of trees in the West African Sahara and Sahel","volume":"587","author":"Brandt","year":"2020","journal-title":"Nature"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"14259","DOI":"10.1038\/s41598-019-50795-9","article-title":"Whale counting in satellite and aerial images with deep learning","volume":"9","author":"Guirado","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Guirado, E., Alcaraz-Segura, D., Cabello, J., Puertas-Ru\u00edz, S., Herrera, F., and Tabik, S. (2020). Tree Cover Estimation in Global Drylands from Space Using Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12030343"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhang, R., Wang, S., and Wang, F. (2018). Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy. Sensors, 18.","DOI":"10.3390\/s18072013"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Foody, G., Pal, M., Rocchini, D., Garzon-Lopez, C., and Bastin, L. (2016). The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5110199"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","first-page":"2","article-title":"Squandering Water in Drylands: The Water Use Strategy of the Phreatophyte Ziziphus lotus (L.) Lam in a Groundwater Dependent Ecosystem","volume":"108","author":"Querejeta","year":"2021","journal-title":"Am. J. Bot."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1007\/s00442-003-1264-x","article-title":"Shrub spatial aggregation and consequences for reproductive success","volume":"136","author":"Tirado","year":"2003","journal-title":"Oecologia"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(97)00068-8","article-title":"A comparative analysis of nebkhas in central Tunisia and northern Burkina Faso","volume":"22","author":"Tengberg","year":"1998","journal-title":"Geomorphology"},{"key":"ref_52","first-page":"1","article-title":"Channel widths, landslides, faults, and beyond: The new world order of high-spatial resolution Google Earth imagery in the study of earth surface processes","volume":"492","author":"Fisher","year":"2012","journal-title":"Google Earth Virtual Vis. Geosci. Educ. Res."},{"key":"ref_53","first-page":"87","article-title":"A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments","volume":"49","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2016.02.030","article-title":"A new approach for land cover classification and change analysis: Integrating backdating and an object-based method","volume":"177","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2015.11.033","article-title":"A novel approach for quantifying particulate matter distribution on leaf surface by combining SEM and object-based image analysis","volume":"173","author":"Yan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1007\/s12524-018-0807-x","article-title":"Selection of Optimal Object Features in Object-Based Image Analysis Using Filter-Based Algorithms","volume":"46","author":"Colkesen","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lef\u00e8vre, S., Sheeren, D., and Tasar, O. (2019). A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020070"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111354","DOI":"10.1016\/j.rse.2019.111354","article-title":"Auxiliary datasets improve accuracy of object-based land use\/land cover classification in heterogeneous savanna landscapes","volume":"233","author":"Hurskainen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_59","first-page":"218","article-title":"SegOptim\u2014A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data","volume":"76","author":"Marcos","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2014.12.026","article-title":"Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery","volume":"102","author":"Ma","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, X., Du, S., and Ming, D. (2018). Segmentation Scale Selection in Geographic Object-Based Image Analysis. High Spat. Resolut. Remote Sens., 201\u2013228.","DOI":"10.1201\/9780429470196-10"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yang, L., Mansaray, L., Huang, J., and Wang, L. (2019). Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050514"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"341","DOI":"10.18280\/ts.370221","article-title":"A Crop Disease Image Recognition Algorithm Based on Feature Extraction and Image Segmentation","volume":"37","author":"Mao","year":"2020","journal-title":"Traitement Signal"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Csillik","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2015.03.019","article-title":"An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops","volume":"114","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Josselin, D., and Louvet, R. (2019). Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030156"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer Science & Business Media.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Watanabe, T., and Wolf, D.F. (2019, January 9\u201312). Instance Segmentation as Image Segmentation Annotation. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814026"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Demir, A., Yilmaz, F., and Kose, O. (2019, January 3\u20135). Early detection of skin cancer using deep learning architectures: Resnet-101 and inception-v3. Proceedings of the 2019 Medical Technologies Congress (TIPTEKNO), Izmir, Turkey.","DOI":"10.1109\/TIPTEKNO47231.2019.8972045"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2012.01.007","article-title":"Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis","volume":"68","author":"Liu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Nussbaum, S., and Menz, G. (2008). eCognition Image Analysis Software. Object-Based Image Analysis and Treaty Verification, Springer.","DOI":"10.1007\/978-1-4020-6961-1_3"},{"key":"ref_72","unstructured":"Dutta, A., Gupta, A., and Zissermann, A. (2020, December 11). VGG Image Annotator (VIA). Available online: http:\/\/www.robots.ox.ac.uk\/~vgg\/software\/via."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Caesar, H., Uijlings, J., and Ferrari, V. (2018, January 18\u201322). COCO-Stuff: Thing and Stuff Classes in Context. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00132"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Wang, B., Li, C., Pavlu, V., and Aslam, J. (2018, January 17\u201320). A Pipeline for Optimizing F1-Measure in Multi-label Text Classification. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00148"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2953","DOI":"10.1080\/01431160500057764","article-title":"Quality assessment for geo--spatial objects derived from remotely sensed data","volume":"26","author":"Zhan","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/15481603.2018.1426092","article-title":"Geographic object-based image analysis (GEOBIA): Emerging trends and future opportunities","volume":"55","author":"Chen","year":"2018","journal-title":"Gisci. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1038\/s41586-020-2686-x","article-title":"Mapping carbon accumulation potential from global natural forest regrowth","volume":"585","author":"Leavitt","year":"2020","journal-title":"Nature"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1126\/science.aam6527","article-title":"The extent of forest in dryland biomes","volume":"356","author":"Bastin","year":"2017","journal-title":"Science"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"eaao0166","DOI":"10.1126\/science.aao0166","article-title":"Comment on \u201cThe extent of forest in dryland biomes\u201d","volume":"358","author":"Schepaschenko","year":"2017","journal-title":"Science"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"eaao0369","DOI":"10.1126\/science.aao0369","article-title":"Comment on \u201cThe extent of forest in dryland biomes\u201d","volume":"358","author":"Cayuela","year":"2017","journal-title":"Science"},{"key":"ref_82","first-page":"485","article-title":"Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling","volume":"18","author":"Ferrier","year":"2020","journal-title":"Remote Sens. Plant Biodivers."},{"key":"ref_83","first-page":"43","article-title":"How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring","volume":"10","author":"Vihervaara","year":"2017","journal-title":"Glob. Ecol. Conserv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:07:18Z","timestamp":1760159238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,5]]},"references-count":83,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21010320"],"URL":"https:\/\/doi.org\/10.3390\/s21010320","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,5]]}}}