کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5005622 | 1369107 | 2016 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Machine Learning for benthic sand and maerl classification and coverage estimation in coastal areas around the Maltese Islands
ترجمه فارسی عنوان
یادگیری ماشین برای طبقه بندی شن و ماسل بنتن و برآورد پوشش در مناطق ساحلی اطراف جزایر مالت
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
چکیده انگلیسی
Analysis of the seabed composition over a large spatial scale is an interesting yet very challenging task. Apart from the field work involved, hours of video footage captured by cameras mounted on Remote Operated Vehicles (ROVs) have to be reviewed by an expert in order to classify the seabed topology and to identify potential anthropogenic impacts on sensitive benthic assemblages. Apart from being time consuming, such work is highly subjective and through visual inspection alone, a quantitative analysis is highly unlikely to be made. This study investigates the applicability of various Machine Learning techniques for the automatic classification of the seabed into maerl and sand regions from recorded ROV footage. ROV data collected from depths ranging between 50Â m and 140Â m and at 9.5Â km from the northeast coastline of the Maltese Islands, is processed. Through the application of the presented technique, 5.23Â GB of data corresponding to 2Â h and 24Â min of footage which was collected during June 2013, was initially cleaned and classified. An estimate for the percentage cover of the two benthic habitats (sandy seabed and maerl) was also computed by using artifacts encountered during the ROV survey and of known dimensions as a reference. Unlike other automatic seabed mapping techniques, the presented prototype processes video footage captured by a down-facing camera and not through acoustic backscatter. Image data is easier and much cheaper to capture. Promising results that indicate a very good degree of agreement between the true and predicted habitat type distribution values, were obtained.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Applied Research and Technology - Volume 14, Issue 5, October 2016, Pages 338-344
Journal: Journal of Applied Research and Technology - Volume 14, Issue 5, October 2016, Pages 338-344
نویسندگان
Adam Gauci, Alan Deidun, John Abela, Kristian Zarb Adami,