{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:23:51Z","timestamp":1772040231421,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T00:00:00Z","timestamp":1517875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFD0300602"],"award-info":[{"award-number":["2016YFD0300602"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFD0800904"],"award-info":[{"award-number":["2016YFD0800904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Natural Science Foundation of China","award":["61661136003"],"award-info":[{"award-number":["61661136003"]}]},{"name":"the Natural Science Foundation of China","award":["41471285"],"award-info":[{"award-number":["41471285"]}]},{"name":"the Natural Science Foundation of China","award":["41471351"],"award-info":[{"award-number":["41471351"]}]},{"name":"the Special Funds for Technology innovation capacity building sponsored by the Beijing Academy of Agriculture and Forestry Sciences","award":["KJCX20170423"],"award-info":[{"award-number":["KJCX20170423"]}]},{"name":"the Special Funds for Technology innovation capacity building sponsored by the Beijing Academy of Agriculture and Forestry Sciences","award":["KYCXPT201703"],"award-info":[{"award-number":["KYCXPT201703"]}]},{"name":"Beijing Municipal Science and Technology Project","award":["Z161100004516009"],"award-info":[{"award-number":["Z161100004516009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram\u2013Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper.<\/jats:p>","DOI":"10.3390\/rs10020246","type":"journal-article","created":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T15:18:05Z","timestamp":1517930285000},"page":"246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7427-0888","authenticated-orcid":false,"given":"Chengquan","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Anhui University, Hefei 230601, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"}]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Xiaodong","family":"Yang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China"},{"name":"Key Laboratory of Agri-Informatics, Ministry of Agriculture, Beijing 100097, China"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6425-8321","authenticated-orcid":false,"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ayas, S., Dogan, H., Gedikli, E., and Ekinci, M. (2015, January 16\u201319). Microscopic image segmentation based on firefly algorithm for detection of tuberculosis bacteria. Proceedings of the Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey.","DOI":"10.1109\/SIU.2015.7129962"},{"key":"ref_2","unstructured":"Azzari, G., and Lobell, D.B. (2015, January 14\u201318). Satellite estimates of crop area and maize yield in Zambia\u2019s agricultural districts (Abstracts). Proceedings of the AGU Fall Meeting, San Francisco, CA, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.infrared.2015.02.008","article-title":"Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model","volume":"71","author":"Kong","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.optcom.2014.12.048","article-title":"Multi-focus image fusion based on sparse feature matrix decomposition and morphological filtering","volume":"342","author":"Li","year":"2015","journal-title":"Opt. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.infrared.2017.02.005","article-title":"Infrared and visible image fusion based on visual saliency map and weighted least square optimization","volume":"82","author":"Ma","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_6","first-page":"1042","article-title":"Fusion Algorithm for hyperspectral remote sensing image combined with harmonic analysis and gram-schmidt transform","volume":"44","author":"Zhang","year":"2016","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1016\/0031-3203(93)90135-J","article-title":"A review on image segmentation techniques","volume":"26","author":"Pal","year":"1993","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2382","DOI":"10.1109\/TGRS.2013.2260552","article-title":"Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization","volume":"52","author":"Ghamisi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Su, Q., and Hu, Z. (2013). Color image quantization algorithm based on self-adaptive differential evolution. Comput. Intell. Neurosci.","DOI":"10.1155\/2013\/231916"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4788","DOI":"10.1109\/TIP.2013.2277832","article-title":"Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy\u2014A differential evolution approach","volume":"22","author":"Sarkar","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s00138-015-0679-9","article-title":"A comparative experimental study of image feature detectors and descriptors","volume":"26","author":"Mukherjee","year":"2015","journal-title":"Mach. Vis. Appl."},{"key":"ref_12","unstructured":"Zhy, C., Gu, G., Liu, H., Shen, J., and Yu, H. (2008, January 12\u201314). Segmentation of ultrasound image based on texture feature and graph cut. Proceedings of the 2008 International Conference on Computer Science and Software Engineering, Wuhan, China."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/TMM.2015.2500727","article-title":"Image retargeting for preserving robust local feature: Application to mobile visual search","volume":"18","author":"Tan","year":"2015","journal-title":"IEEE Trans. Multimed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1109\/TIP.2012.2186306","article-title":"Medical image segmentation by combining graph cuts and oriented active appearance models","volume":"21","author":"Chen","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Narkhede, P.R., and Gokhale, A.V. (2015, January 28\u201330). Color image segmentation using edge detection and seeded region growing approach for CIELab and HSV color spaces. Proceedings of the International Conference on Industrial Instrumentation and Control, Pune, India.","DOI":"10.1109\/IIC.2015.7150932"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TFUZZ.2013.2249072","article-title":"Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images","volume":"22","author":"Gong","year":"2014","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pahariya, S., and Tiwari, S. (2015, January 10\u201312). Image segmentation using snake model with nosie adaptive fuzzy switching median filter and MSRM method. Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India.","DOI":"10.1109\/ICCIC.2015.7435798"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1016\/j.patrec.2011.07.028","article-title":"Entropy based region selection for moving object detection","volume":"32","author":"Subudhi","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"354","DOI":"10.4028\/www.scientific.net\/AMM.741.354","article-title":"The detection method of lane line based on the improved Otsu threshold segmentation","volume":"741","author":"Tang","year":"2015","journal-title":"Appl. Mech. Mater."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, W., Ye, Z., Wang, M., Ma, L., and Liu, W. (2015, January 24\u201326). An image threholding approach based on cuckoo search algorithm and 2D maximum entropy. Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Warsaw, Poland.","DOI":"10.1109\/IDAACS.2015.7340748"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Dai, Y., Xue, J., Liu, B., Ma, C., and Gao, Y. (2017). Research of segmentation method on color image of Lingwu long jujubes based on the maximum entropy. EURASIP J. Image Video Process., 1.","DOI":"10.1186\/s13640-017-0182-5"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.proenv.2015.03.032","article-title":"The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia","volume":"24","author":"Rokhmana","year":"2015","journal-title":"Procedia Environ. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1137\/16M1057152","article-title":"Minimum cuts and shortest cycles in directed planar graphs via noncrossing shortest paths","volume":"31","author":"Liang","year":"2017","journal-title":"SIAM J. Discret. Math."},{"key":"ref_24","unstructured":"Sang, N.T., Van, T.M.N., Van, A.H., and Lung, V.D. (2015, January 23\u201324). A novel method for video enhancement-RGB local context-based fusion. Proceedings of the 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealand."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, D., Kang, X., and Wang, J. (2012). A novel image de-noising method based on spherical coordinates system. EURASIP J. Adv. Signal Process., 2012.","DOI":"10.1186\/1687-6180-2012-110"},{"key":"ref_26","unstructured":"Suzuki, T., Tsuji, H., Taguchi, A., and Kimura, T. (2014). A Estimate method of standard deviation for Gaussian noise with image information. Institute of Electronics, Information and Communication Engineers (IEICE) Technical Report, Institute of Electronics, Information and Communication Engineers. Intelligent Multimedia System, General."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gao, L., Wang, G., and Liu, J. (2015). Image denoising based on edge detection and prethresholding wiener filtering of multi-wavelets fusion. Int. J. Wavelets Multiresolut. Inf. Process., 13.","DOI":"10.1142\/S0219691315500319"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"El Hassani, A., and Majda, A. (2016, January 24\u201326). Efficient image denoising method based on mathematical morphology reconstruction and the Non-Local Means filter for the MRI of the head. Proceedings of the 4th IEEE International Colloquium on Information Science and Technology (CiSt), Tangier, Morocco.","DOI":"10.1109\/CIST.2016.7805084"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"19781","DOI":"10.1007\/s11042-015-3192-2","article-title":"Improved image segmentation method based on morphological reconstruction","volume":"76","author":"Wu","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5730","DOI":"10.1109\/TIP.2017.2740566","article-title":"Nonlocally multi-morphological representation for image reconstruction from compressive measurements","volume":"26","author":"Wu","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hunger, S., Karrasch, P., and Wessollek, C. (2016, January 25). Evaluating the potential of image fusion of multispectral and radar remote sensing data for the assessment of water body structure. Proceedings of the SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII 999814, Edinburgh, UK.","DOI":"10.1117\/12.2241264"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ferris, M.H., McLaughlin, M., Grieggs, S., Ezekiel, S., Blasch, E., Alford, M., Cornacchia, M., and Bubalo, A. (2015, January 15\u201319). Using ROC curves and AUC to evaluate performance of no-reference image fusion metrics. In Proceeding of the 2015 National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA.","DOI":"10.1109\/NAECON.2015.7443034"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Begill, A., and Arora, S. (2015, January 21\u201323). Evaluating the short comings of the various digital image fusion algorithms. In Proceeding of the 2015 IEEE International Conference on Electro\/Information Technology (EIT), Dekalb, IL, USA.","DOI":"10.1109\/EIT.2015.7293418"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1109\/TIM.2003.814821","article-title":"A quantitative method for evaluating the performances of hyperspectral image fusion","volume":"52","author":"Wang","year":"2003","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, X., and Chen, J. (2005, January 4). An efficient method to evaluate fusion performance of remote sensing image. Proceedings of the SPIE 6044, MIPPR 2005: Image Analysis Techniques, 60440I, Wuhan, China.","DOI":"10.1117\/12.654526"},{"key":"ref_36","first-page":"42","article-title":"Objective analysis and evaluation of remote sensing image fusion effect","volume":"26","author":"Li","year":"2004","journal-title":"Comput. Eng. Sci."},{"key":"ref_37","first-page":"2393","article-title":"Remote sensing image fusion algorithm based on Shearlet transform and region segmentation","volume":"26","author":"Zhang","year":"2015","journal-title":"J. Optoelectron. Laser"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wan, T., Qin, Z., Zhu, C., and Liao, R. (2013, January 26\u201331). A robust fusion scheme for multifocus images using sparse features. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6637995"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2015.10.080","article-title":"Image fusion with saliency map and interest points","volume":"177","author":"Meng","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1088\/0957-0233\/5\/4\/013","article-title":"A study of the effect of image quality on texture energy measures","volume":"5","author":"Benke","year":"1994","journal-title":"Meas. Sci. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.cviu.2007.04.003","article-title":"A feature-based metric for the quantitative evaluation of pixel-level image fusion","volume":"109","author":"Zheng","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Luo, P., Loy, C.C., and Tang, X. (2015, January 7\u201313). Semantic image segmentation via deep parsing network. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.162"},{"key":"ref_43","first-page":"128","article-title":"Multiscale combinatorial grouping for image segmentation and object proposal generation","volume":"39","author":"Barron","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1111\/jmi.12186","article-title":"Performance evaluation of image segmentation algorithms on microscopic image data","volume":"257","year":"2015","journal-title":"J. Microsc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"41","DOI":"10.5815\/ijigsp.2017.01.06","article-title":"Performance evaluation of image segmentation method based on doubly truncated generalized Laplace Mixture Model and hierarchical clustering","volume":"9","author":"Jyothirmayi","year":"2017","journal-title":"Int. J. Image Graphics Signal Process."},{"key":"ref_46","first-page":"34","article-title":"Effect of light intensity on Epinephelus malabaricus's image segmentation processing","volume":"1","author":"Xu","year":"2016","journal-title":"J. Tianjin Agric. Univ."},{"key":"ref_47","first-page":"78","article-title":"Method of color image segmentation based on color constancy","volume":"1","author":"Wang","year":"2015","journal-title":"J. Northeast Dianli Uiniv. (Nat. Sci. Ed.)"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/36.951105","article-title":"Detection of small objects from high-resolution panchromatic satellite imagery based on supervised image segmentation","volume":"39","author":"Segl","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kwok, N.M., Ha, Q.P., and Fang, G. (2009, January 17\u201319). Effect of color space on color image segmentation. Proceedings of the 2009 2nd International Congress on Image and Signal Processing, Tianjin, China.","DOI":"10.1109\/CISP.2009.5304250"},{"key":"ref_50","unstructured":"Gao, Y., Kerle, N., Mas, J.F., Navarrete, A., and Niemeyer, I. (2011, July 14). Optimized Image Segmentation and Its Effect on Classification Accuracy. Available online: http:\/\/www.isprs.org\/proceedings\/xxxvi\/2-c43\/Postersession\/gao_kerle_et_al.pdf."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lei, T., and Sewchand, W. (1990, January 30). An Investigation into the effect of independence of pixel images on image segmentation. In Proceeding of the SPIE 1153, Applications of Digital Image Processing XII, San Diego, CA, USA.","DOI":"10.1117\/12.962337"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3934\/ipi.2012.6.95","article-title":"A multiphase logic framework for multichannel image segmentation","volume":"6","author":"Keegan","year":"2012","journal-title":"Inverse Probl. Imaging"},{"key":"ref_53","first-page":"4024","article-title":"Image segmentation by generalized hierarchical fuzzy C-means algorithm","volume":"28","author":"Zheng","year":"2015","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"153","DOI":"10.54386\/jam.v18i1.923","article-title":"Maize yield estimation using agro-meteorological variables in Jaunpur district of Eastern Uttar Pradesh","volume":"18","author":"Tripathi","year":"2016","journal-title":"J. Agrometeorol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/TIP.2009.2021087","article-title":"An edge-weighted centroidal Voronoi tessellation model for image segmentation","volume":"18","author":"Wang","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1109\/TGRS.2005.846874","article-title":"A comparative analysis of image fusion methods","volume":"43","author":"Wang","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","unstructured":"Xu, M., Guo, M., Shang, L., and Jia, X. (2016, January 24\u201329). Multi-value image segmentation based on FCM algorithm and Graph Cut Theory. Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada."},{"key":"ref_58","first-page":"855","article-title":"An efficient method of SAR image segmentation based on texture feature","volume":"16","author":"Xue","year":"2016","journal-title":"J. Comput. Methods Sci. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yang, Y., Han, C., Kang, X., and Han, D. (2007, January 18\u201321). An overview on Pixel-Level image fusion in remote sensing. Proceedings of the 2007 IEEE International Conference on Automation and Logistics, Jinan, China.","DOI":"10.1109\/ICAL.2007.4338968"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yu, C., Jin, B., Lu, Y., Chen, X., Yi, Z., Kai, Z., and Wang, S. (2013, January 16\u201318). Multi-threshold image segmentation based on firefly algorithm. Proceedings of the 2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Beijing, China.","DOI":"10.1109\/IIH-MSP.2013.110"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/246\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:53:56Z","timestamp":1760194436000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,6]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["rs10020246"],"URL":"https:\/\/doi.org\/10.3390\/rs10020246","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,6]]}}}