Remote sensing technology has become an important way to obtain agricultural greenhouse coverage information quickly and effectively.But the spatial resolution size of remote sensing images has a dual influence on the extraction accuracy,and it is important to select suitable resolution images.Taking the southern agricul-tural plastic greenhouses as the research object,GF-1,GF-2 and Sentinel-2 are used to form six different spa-tial resolution image datasets between 1 and 16 m.Based on Object-Based Image Analysis(OBIA),we use the Convolutional Neural Network(CNN)and Random Forest(RF)methods to extract the canopy and ana-lyze the extraction accuracy and the difference between the methods.The results show that:(1)the extraction accuracy of agricultural greenhouses under CNN and RF methods generally decreases as the image resolution de-creases,and agricultural sheds can be detected on images from 1m to 16 m;(2)the CNN method requires high-er spatial resolution than the RF method,and the CNN method has fewer missed and false extractions at 1～2 m resolution,but at 4 m and lower resolutions,the RF method is more applicable;(3)the 2 m resolution im-age is the best spatial resolution for shed information extraction,which can realize shed monitoring economically and effectively.
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