通过集成“简单非迭代聚类（SNIC）超像素”与“Canny边缘检测方法”的农田边界检测
Agricultural Field Boundary Detection through Integration of "Simple Non-Iterative Clustering (SNIC) Super Pixels" and "Canny Edge Detection Method"
AI总结：
本文提出将SNIC超像素与Canny边缘检测相结合，利用Sentinel-2数据和Google Earth Engine平台实现农田边界的准确可靠检测与分类。
摘要：
高效利用耕地是实现农业可持续发展和保障粮食安全的必要因素。随着发达国家卫星技术的快速发展，人们正在探索新的方法以准确、可操作地识别耕地。在此背景下，基于卫星图像数据光谱分析的农田边界识别被认为是现代农业中最优且最准确的方法之一。本文提出了一种利用通过“Google Earth Engine”（GEE）平台获取的卫星数据来确定耕地适宜性和绿色指数的新方法。在该方法中，两种强大的算法——“SNIC（简单非迭代聚类）超像素”和“Canny边缘检测方法”——被结合在一起。SNIC算法将卫星图像中的像素合并为具有相似特征的更大区域（超像素），从而提供更好的图像分析效果。Canny边缘检测方法检测图像中的剧烈变化（边缘），以确定农田的精确边界。本研究使用来自Sentinel-2卫星的高分辨率多光谱数据和Google Earth Engine JavaScript API开展，结果表明所提方法能够准确、可靠地对随机选择的农田进行分类。这两种工具的结合使用通过最小化卫星图像中异常值的影响，能够更准确地确定农田边界。因此，基于所获得的数据，可以为大范围农业监测和资源管理创建更准确、更可靠的地图。这拓展了基于云的平台和人工智能方法在农业领域的应用能力。
英文摘要：
Efficient use of cultivated areas is a necessary factor for sustainable development of agriculture and ensuring food security. Along with the rapid development of satellite technologies in developed countries, new methods are being searched for accurate and operational identification of cultivated areas. In this context, identification of cropland boundaries based on spectral analysis of data obtained from satellite images is considered one of the most optimal and accurate methods in modern agriculture. This article proposes a new approach to determine the suitability and green index of cultivated areas using satellite data obtained through the "Google Earth Engine" (GEE) platform. In this approach, two powerful algorithms, "SNIC (Simple Non-Iterative Clustering) Super Pixels" and "Canny Edge Detection Method", are combined. The SNIC algorithm combines pixels in a satellite image into larger regions (super pixels) with similar characteristics, thereby providing better image analysis. The Canny Edge Detection Method detects sharp changes (edges) in the image to determine the precise boundaries of agricultural fields. This study, carried out using high-resolution multispectral data from the Sentinel-2 satellite and the Google Earth Engine JavaScript API, has shown that the proposed method is effective in accurately and reliably classifying randomly selected agricultural fields. The combined use of these two tools allows for more accurate determination of the boundaries of agricultural fields by minimizing the effects of outliers in satellite images. As a result, more accurate and reliable maps can be created for agricultural monitoring and resource management over large areas based on the obtained data. By expanding the application capabilities of cloud-based platforms and artificial intelligence methods in the agricultural field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
