Abstract: Food security is the foundation for the long-term stability and prosperity of a country.Leveraging the advantages of high spatial resolution remote sensing imagery, which covers a wide range and possesses both spectral and textural information, this paper combines textural feature extraction based on gray-level co-occurrence matrices with geometric feature extraction using the Hough transform line detection algorithm.Focusing on the characteristic manifestations of agricultural greenhouses in the imagery, a local area in Ningbo is selected as the study area to conduct information extraction experiments and validations.For sub-meter high spatial resolution remote sensing imagery, an average extraction accuracy of approximately 90% can be achieved, effectively reducing the impact of spectral reflectance differences on recognition accuracy.Compared to object-oriented extraction methods based on image segmentation and neural network extraction methods based on deep learning, the method proposed in this paper exhibits higher feature intuitiveness and comprehensibility, reduces computational complexity and requirements for sample volume, and is conducive to rapid and accurate interpretation and extraction of agricultural greenhouses within cultivated land areas.

Key words: cultivated land, greenhouse, remote sensing, texture feature, geometric feature