摘要:
在全球气候变暖背景下,愈发频繁的洪涝灾害是造成我国粮食作物减产的主要农业灾害之一。雷达遥感技术具备全天候的对地观测能力,是快速监测区域范围内洪涝灾害信息的一种重要手段。随着人工智能领域的发展,机器学习方法广泛应用于洪涝灾害遥感监测,虽然该类算法具有较高的精度,但其训练过程往往需要大量的野外调查或遥感解译样本支持。为克服样本标记限制、提高区域尺度洪涝灾害监测的精度,本研究以2021年7月20日河南北部特大洪涝事件为背景,利用国产高分三号(GF-3)双极化雷达影像(HH-HV),构建了一种基于弱监督高斯混合模型(gaussian mixture model,GMM)的洪涝淹没作物监测方法,通过该方法提取了豫北部分区域农田洪涝淹没范围。通过对比4种典型的机器学习方法,包括随机森林、支持向量机、K最近邻分类和平行六面体方法,发现该文构建的弱监督GMM方法的精度最高,其总体精度为0.95,Kappa系数为0.90。该研究对于提高基于合成孔径雷达(synthetic aperture radar,SAR)遥感技术监测区域尺度作物洪涝的准确性和普适性具有重要意义。
Abstract:
Against the backdrop of global warming, increasingly frequent floods become a primary agricultural disaster that causes reduced crop production in China. Radar remote sensing technology, possessing all-weather earth observation capabilities, serves as a critical means for rapid monitoring of regional flood information. With the advancement in artificial intelligence, machine learning methods have been extensively applied in the remote sensing-based monitoring of floods. Despite the high accuracy of their algorithms, their training processes often entail extensive field investigations or numerous samples for remote sensing image interpretation. This study aims to overcome sample labeling limitations and improve regional flood monitoring accuracy. Based on the catastrophic flood that occurred in northern Henan on July 20, 2021, this study constructed a flooded crop monitoring method based on the weakly supervised Gaussian mixture model (GMM) using domestic GF-3 HH-HV radar images. Then this method was applied to extract the flooding range of farmland in some areas of northern Henan. Compared to four typical machine learning methods, i.e., random forest (RF), support vector machine (SVM), K-nearest neighbor classification, and parallelepiped classification, the weakly supervised GMM in this study enjoyed the highest accuracy, with overall precision of 0.95 and a Kappa coefficient of 0.90. This study holds great significance for enhancing the accuracy and universality of regional crop flooding monitoring based on remote sensing technology and synthetic aperture radars (SARs).
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