{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:46:26Z","timestamp":1770749186114,"version":"3.50.0"},"reference-count":24,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T00:00:00Z","timestamp":1499904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601481"],"award-info":[{"award-number":["41601481"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012541","name":"Guangdong Innovative and Entrepreneurial Research Team Program","doi-asserted-by":"publisher","award":["2016ZT06D336"],"award-info":[{"award-number":["2016ZT06D336"]}],"id":[{"id":"10.13039\/100012541","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Guangdong Academy of Sciences","award":["QNJJ201601"],"award-info":[{"award-number":["QNJJ201601"]}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2016A020210060"],"award-info":[{"award-number":["2016A020210060"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"name":"GDAS' Special Project of Science and Technology Development","award":["2017GDASCX-0101"],"award-info":[{"award-number":["2017GDASCX-0101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU) to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA\u2019s compute unified device architecture (CUDA) through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 \u00d7 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application.<\/jats:p>","DOI":"10.3390\/rs9070721","type":"journal-article","created":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T10:31:57Z","timestamp":1499941917000},"page":"721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5122-0412","authenticated-orcid":false,"given":"Hao","family":"Jiang","sequence":"first","affiliation":[{"name":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China"}]},{"given":"Shuisen","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China"}]},{"given":"Dan","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China"}]},{"given":"Chongyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"Guangzhou Institute of Geochemistry, Guangzhou 510640, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Ji","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"Guangzhou Institute of Geochemistry, Guangzhou 510640, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9632","DOI":"10.3390\/rs70809632","article-title":"Inventory of small forest areas using an unmanned aerial system","volume":"7","author":"Puliti","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1080\/01431161003777205","article-title":"Individual tree detection based on variable and fixed window size local maxima filtering applied to ikonos imagery for even-aged eucalyptus plantation forests","volume":"32","author":"Gebreslasie","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.rse.2007.02.029","article-title":"Single tree detection in very high resolution remote sensing data","volume":"110","author":"Hirschmugl","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"22643","DOI":"10.3390\/s141222643","article-title":"Tree crown mapping in managed woodlands (parklands) of semi-arid west africa using worldview-2 imagery and geographic object based image analysis","volume":"14","author":"Karlson","year":"2014","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4692","DOI":"10.1109\/JSTARS.2014.2331425","article-title":"Efficient framework for palm tree detection in uav images","volume":"7","author":"Malek","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1016\/S0167-8655(98)00092-0","article-title":"Optimizing templates for finding trees in aerial photographs","volume":"19","author":"Larsen","year":"1998","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","unstructured":"Descombes, X., and Pechersky, E. (2006). Tree Crown Extraction Using a Three State Markov Random Field, INRIA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.foreco.2012.10.007","article-title":"Mapping local density of young eucalyptus plantations by individual tree detection in high spatial resolution satellite images","volume":"301","author":"Jia","year":"2013","journal-title":"For. Ecol. Manag."},{"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","first-page":"5827","DOI":"10.1080\/01431161.2010.507790","article-title":"Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions","volume":"32","author":"Larsen","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1023\/A:1008045108935","article-title":"Feature detection with automatic scale selection","volume":"30","author":"Lindeberg","year":"1998","journal-title":"Int. J. Comput. Vis."},{"key":"ref_12","first-page":"157","article-title":"Pycuda and pyopencl: A scripting-based approach to gpu run-time code generation","volume":"38","author":"Ckner","year":"2011","journal-title":"Parallel Comput."},{"key":"ref_13","first-page":"270","article-title":"The stratigraphy and chronology of multicycle quaternary volcanic rock-red soil sequence in Leizhou Peninsula, South China","volume":"21","author":"Zhu","year":"2001","journal-title":"Quat. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Walczykowski, P., and Kedzierski, M. (2016, January 21). Imagery Intelligence from Low Altitudes: Chosen Aspects. Proceedings of the XI Conference on Reconnaissance and Electronic Warfare Systems, Oltarzew, Poland.","DOI":"10.1117\/12.2269318"},{"key":"ref_15","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_16","unstructured":"Ford, A., and Roberts, A. (2017, May 17). Colour Space Conversions. Available online: http:\/\/sites.biology.duke.edu\/johnsenlab\/pdfs\/tech\/colorconversion.pdf."},{"key":"ref_17","unstructured":"Ford, A. (1998). Colour Space Conversions, Westminster University."},{"key":"ref_18","unstructured":"Weisstein, E.W. (2017, May 17). Circle-Circle Intersection. Available online: http:\/\/mathworld.wolfram.com\/Circle-CircleIntersection.html."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"Scikit-image: Image processing in python","volume":"2","author":"Walt","year":"2014","journal-title":"PeerJ"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MCSE.2010.118","article-title":"Cython: The best of both worlds","volume":"13","author":"Behnel","year":"2010","journal-title":"Comput. Sci. Eng."},{"key":"ref_21","first-page":"14","article-title":"Comparation of several cuda accelerated gaussian filtering algorithms","volume":"49","author":"Liu","year":"2013","journal-title":"Comput. Eng. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, H., and Ma, Y. (2013). Imbalanced Learning: Foundations, Algorithms, and Applications, John Wiley & Sons, Inc.","DOI":"10.1002\/9781118646106"},{"key":"ref_23","first-page":"530","article-title":"Learning to detect natural image boundaries using local brightness, color, and texture cues","volume":"26","author":"Martin","year":"2004","journal-title":"IEEE Comput. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.measurement.2016.06.003","article-title":"Methodology of improvement of radiometric quality of images acquired from low altitudes","volume":"92","author":"Kedzierski","year":"2016","journal-title":"Measurement"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/7\/721\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:42:36Z","timestamp":1760208156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/7\/721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,13]]},"references-count":24,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2017,7]]}},"alternative-id":["rs9070721"],"URL":"https:\/\/doi.org\/10.3390\/rs9070721","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,13]]}}}