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Corn and soybeans play pivotal roles in the agricultural landscape of the United States, and accurately delineating their cultivation areas is indispensable for ensuring food security and addressing hunger-related challenges. Traditional methods for crop mapping are both labor-intensive and time-consuming. Fortunately, the advent of high-resolution imagery, exemplified by Sentinel-2A (S2A), has opened avenues for precise identification of these crops at a field scale, with the added advantage of cloud computing. This paper presents an innovative algorithm designed for large-scale mapping of corn and soybean planting areas on the Google Cloud Engine, drawing inspiration from symmetrical theory. The proposed methodology encompasses several sequential steps. First, S2A data undergo processing incorporating phenological information and spectral characteristics. Subsequently, texture features derived from the grayscale matrix are synergistically integrated with spectral features in the first step. To enhance algorithmic efficiency, the third step involves a feature importance analysis, facilitating the retention of influential bands while eliminating redundant features. The ensuing phase employs three base classifiers for feature training, and the final result maps are generated through a collective voting mechanism based on the classification results from the three classifiers. Validation of the proposed algorithm was conducted in two distinct research areas: Ford in Illinois and White in Indiana, showcasing its commendable classification capabilities for these crops. The experiments underscore the potential of this method for large-scale mapping of crop areas through the integration of cloud computing and high-resolution imagery.
Lv et al. (Tue,) studied this question.