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Machine learning-based crop recognition from aerial remote sensing imagery

Frontiers of Earth Science 2021年第1期15卷 54-69页

作者： Yanqin TIAN Chenghai YANG Wenjiang HUANG Jia TANG Xingrong LI Qing ZHANG Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China USDA-Agricultural Research Service Aerial Application Technology Research UnitCollege StationTX 77845USA

Timely and accurate acquisition of crop distribution and planting area information is important for making agricultural planning and management *** study employed aerial imagery as a data source and machine learning as a classification tool to statically and dynamically identify crops over an agricultural cropping *** analysis of pixel-based and object-based classifications was performed and classification results were further refined based on three types of object features(layer spectral,geometry,and texture).static recognition using layer spectral features had the highest accuracy of 75.4%in object-based classification,and dynamic recognition had the highest accuracy of 88.0%in object-based classification based on layer spectral and geometry *** identification could not only attenuate the effects of variations on planting dates and plant growth conditions on the results,but also amplify the differences between different ***-based classification produced better results than pixel-based classification,and the three feature sets(layer spectral alone,layer spectral and geometry,and all three)resulted in only small differences in accuracy in object-based *** recognition combined with objectbased classification using layer spectral and geometry features could effectively improve crop classification accuracy with high resolution aerial *** methodologies and results from this study should provide practical guidance for crop identification and other agricultural mapping applications.

关键词： machine learning crop recognition aerial imagery dynamic recognition static recognition

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