Author(s)

Xinyi Yang

Corresponding Author

Xinyi Yang

ABSTRACT

The quantity and quality of cultivated land in a country is the key to maintaining sustainable agricultural development. Satellite remote sensing images can be used to identify and extract cultivated land and conduct remote sensing mapping of cultivated land. Accurate distribution of cultivated land can provide important support for national decision-making departments. This paper uses a color model based on RGB and HSV conversion and uses MATLAB to extract non-cultivated land images for data binarization and denoising processing operations and introduces regional image segmentation algorithms, watershed algorithms, and clustering algorithms to solve the problem of unclear boundaries further. The C++ image processing algorithm is built based on Visual Studio and OpenCV, and the proportion of cultivated land is determined by calculating the proportion of black and white pixels of the annotated picture. The actual cultivated area is further estimated to achieve good results.

KEYWORDS

Remote sensing image segmentation, color model based on RGB and HSV conversion, regionalized image segmentation algorithm, watershed algorithm