{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:31:17Z","timestamp":1760369477292,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,23]],"date-time":"2019-01-23T00:00:00Z","timestamp":1548201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2017YFB0503805"],"award-info":[{"award-number":["2017YFB0503805"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Compared to multispectral or panchromatic bands, fusion imagery contains both the spectral content of the former and the spatial resolution of the latter. Even though the Estimation of Scale Parameter (ESP), the ESP 2 tool, and some segmentation evaluation methods have been introduced to simplify the choice of scale parameter (SP), shape, and compactness, many challenges remain, including obtaining the natural border of plastic greenhouses (PGs) from a GaoFen-2 (GF-2) fusion imagery, accelerating the progress of follow-up texture analysis, and accurately evaluating over-segmentation and under-segmentation of PG segments in geographic object-based image analysis. Considering the features of high-resolution images, the heterogeneity of fusion imagery was compressed using texture analysis before calculating the optimal scale parameter in ESP 2 in this study. As a result, we quantified the effects of image texture analysis, including increasing averaging operator size (AOS) and decreasing greyscale quantization level (GQL) on PG segments via recognition of a proposed Over-Segmentation Index (OSI)-Under-Segmentation Index (USI)-Error Index of Total Area (ETA)-Composite Error Index (CEI) pattern. The proposed pattern can be used to reasonably evaluate the quality of PG segments obtained from GF-2 fusion imagery and its derivative images, showing that appropriate texture analysis can effectively change the heterogeneity of a fusion image for better segmentation. The optimum setup of GQL and AOS are determined by comparing CEI and visual analysis.<\/jats:p>","DOI":"10.3390\/rs11030231","type":"journal-article","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T11:12:48Z","timestamp":1548328368000},"page":"231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Evaluating the Effects of Image Texture Analysis on Plastic Greenhouse Segments via Recognition of the OSI-USI-ETA-CEI Pattern"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0666-1611","authenticated-orcid":false,"given":"Yao","family":"Yao","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.20 Datun Road, Chaoyang District, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing 100049, China"}]},{"given":"Shixin","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.20 Datun Road, Chaoyang District, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","article-title":"TurboPixels: Fast superpixels using geometric flows","volume":"31","author":"Levinshtein","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.patrec.2013.09.013","article-title":"An evaluation of the compactness of superpixels","volume":"43","author":"Schick","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jvcir.2014.11.005","article-title":"The image segmentation based on optimized spatial feature of superpixel","volume":"26","author":"Tian","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2009.71","article-title":"Watershed cuts: Thinnings, shortest path forests, and topological watersheds","volume":"32","author":"Cousty","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.jvcir.2015.09.015","article-title":"Automated coronal hole segmentation from Solar EUV Images using the watershed transform","volume":"33","author":"Ciecholewski","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_8","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2000). Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung, Herbert Wichmann Verlag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4625","DOI":"10.1080\/01431160701241746","article-title":"Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition","volume":"28","author":"Tian","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","unstructured":"Trimble (2017). eCognition Developer 9.3 Reference Book, Trimble Germany GmbH."},{"key":"ref_11","unstructured":"Nixon, M.S., and Aguado, A.S. (2012). Feature Extraction & Image Processing for Computer Vision, Elservier and Pte Ltd.. [3rd ed.]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.neucom.2013.09.058","article-title":"Geometric active curve for selective entropy optimization","volume":"139","author":"Gao","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.sigpro.2016.12.021","article-title":"Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation","volume":"134","author":"Ding","year":"2017","journal-title":"Signal Process."},{"key":"ref_14","unstructured":"Neubert, M., Herold, H., and Meinel, G. (2008). Assessing Image Segmentation Quality\u2014Concepts, Methods and Application, Springer."},{"key":"ref_15","unstructured":"Neubert, M., and Herold, H. (2008, January 6\u20137). Assessment of remote sensing image segmentation quality. Proceedings of the GEOBIA, Calgary, AB, Canada."},{"key":"ref_16","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_17","first-page":"145","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_18","doi-asserted-by":"crossref","unstructured":"Aguilar, M.A., Novelli, A., Nemamoui, A., Aguilar, F.J., Garc\u00eda Lorca, A., and Gonz\u00e1lez-Yebra, \u00d3. (2017, January 20\u201322). Optimizing Multiresolution Segmentation for Extracting Plastic Greenhouses from WorldView-3 Imagery. Proceedings of the Intelligent Interactive Multimedia Systems and Services, Gold Coast, Australia.","DOI":"10.1007\/978-3-319-59480-4_4"},{"key":"ref_19","first-page":"183","article-title":"Object-Based Greenhouse Classification from High Resolution Satellite Imagery: A Case Study Antalya-Turkey","volume":"XLI-B7","author":"Coslu","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_20","first-page":"79","article-title":"Object-based classification approach for greenhouse mapping using Landsat-8 imagery","volume":"9","author":"Wu","year":"2016","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_22","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":"Dragut","year":"2014","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"ref_23","first-page":"580","article-title":"Specific target objects\u2014Specific scale levels? Application of the estimation of scale parameter 2 (ESP 2) tool for the identification of scale levels for distinct target objects","volume":"3","author":"Tiede","year":"2014","journal-title":"South-East. Eur. J. Earth Obs. Geomat."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/14498596.2010.487850","article-title":"Enhanced evaluation of image segmentation results","volume":"55","author":"Marpu","year":"2010","journal-title":"J. Spat. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1080\/01431160701469016","article-title":"Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery","volume":"29","author":"Su","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.isprsjprs.2008.03.003","article-title":"Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses","volume":"63","author":"Aguilar","year":"2008","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, L., Wu, W., Jiang, Z., and Li, H. (2016). Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features. Remote Sens., 8.","DOI":"10.3390\/rs8040353"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Texture features for image classifications","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aguilar, M.A., Nemmaoui, A., Novelli, A., Aguilar, F.J., and Garc\u00eda Lorca, A. (2016). Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8060513"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.cviu.2007.08.003","article-title":"Image segmentation evaluation: A survey of unsupervised methods","volume":"110","author":"Zhang","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/LGRS.2011.2163056","article-title":"An Unsupervised Evaluation Method for Remotely Sensed Imagery Segmentation","volume":"9","author":"Zhang","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gao, H., Tang, Y., Jing, L., Li, H., and Ding, H. (2017). A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images. Sensor, 17.","DOI":"10.3390\/s17102427"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Qi, Q., and Liu, Y. (2018). Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10081193"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, L., Albregtsen, F., L\u00f8nnestad, T., and Gr\u00f8ttum, P. (1995). A supervised approach to the evaluation of image segmentation methods. Comput. Anal. Images Patterns, 759\u2013765.","DOI":"10.1007\/3-540-60268-2_377"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/0031-3203(95)00169-7","article-title":"A survey on evaluation methods for image segmentation","volume":"29","author":"Zhang","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_36","unstructured":"Chabrier, S., Laurent, H., Emile, B., Rosenberger, C., and Marche, P. (2004, January 6\u201310). A comparative study of supervised evaluation criteria for image segmentation. Proceedings of the EUSIPCO, Vienna, Austria."},{"key":"ref_37","unstructured":"Correia, P., and Pereira, F. (2000, January 10\u201313). Objective evaluation of relative segmentation. Proceedings of the International Conference on Image Processing, Vancouver, BC, Canada."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1109\/TGRS.2002.805072","article-title":"Existential uncertainty of spatial objects segmented from satellite sensor imagery","volume":"40","author":"Lucieer","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","first-page":"311","article-title":"The comparison index: A tool for assessing the accuracy of image segmentation","volume":"9","author":"Lymburner","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"289","DOI":"10.14358\/PERS.76.3.289","article-title":"Accuracy Assessment Measures for Object-based Image Segmentation Goodness","volume":"76","author":"Clinton","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TGRS.2009.2029570","article-title":"A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images","volume":"48","author":"Persello","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Y.D., Huang, Z., Wang, M.M., Yang, D., Ma, H.M., Zhang, Y.X., Li, Y.F., Li, H.W., and Hu, X.G. (2016, January 14\u201316). Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis. Proceedings of the GEOBIA 2016: Solutions and Synergies, Enschede, The Netherlands.","DOI":"10.3990\/2.452"},{"key":"ref_44","first-page":"125","article-title":"Comparison of plastic greenhouse extraction method based on GF-2 remote-sensing imagery","volume":"23","author":"Gao","year":"2018","journal-title":"J. China Agric. Univ."},{"key":"ref_45","first-page":"403","article-title":"Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almer\u00eda (Spain)","volume":"52","author":"Novelli","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cai, L., Shi, W., Miao, Z., and Hao, M. (2018). Accuracy Assessment Measures for Object Extraction from Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10020303"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.04.002","article-title":"A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches","volume":"141","author":"Ye","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"25","DOI":"10.14358\/PERS.84.10.629","article-title":"Review on High Spatial Resolution Remote Sensing Image Segmentation Evaluation","volume":"84","author":"Chen","year":"2018","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2017.03.002","article-title":"Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index","volume":"128","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","unstructured":"China Centre For Resources Satellite Data and Application (2015, November 05). GF-2. Available online: http:\/\/www.cresda.com\/EN\/satellite\/7157.shtml."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/36.469481","article-title":"Evaluation of textural and multipolarization radar features for crop classification","volume":"33","author":"Anys","year":"1995","journal-title":"IEEE Trans. Geosicence Remote Sens."},{"key":"ref_52","unstructured":"Kim, M., Madden, M., and Warner, T. (2008). Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery, Springer."},{"key":"ref_53","unstructured":"Lucieer, A. (2004). Uncertainties in Segmentation and Their Visualisation, International Institute for Geo-Information Science and Earth Observation."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/231\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:28:12Z","timestamp":1760185692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,23]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030231"],"URL":"https:\/\/doi.org\/10.3390\/rs11030231","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,1,23]]}}}