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 agricultural plastic greenhouses as the research object， GF-1， GF-2 and Sentinel-2 are used to form six different spatial 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 analyze 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 decreases， and agricultural sheds can be detected on images from 1m to 16 m； （2） the CNN method requires higher 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 image is the best spatial resolution for shed information extraction， which can realize shed monitoring economically and effectively.