【摘要】 贵州省是我国重要的烟草种植区之一，其喀斯特山区受强云雾干扰且地物破碎，限制了烟草种植区的遥感提取精度。针对上述问题，以贵州省毕节市金沙县为研究区，依托Google Earth Engine平台，采用HSV-PCA方法融合Sentinel-1合成孔径雷达影像与Sentinel-2多光谱影像，构建多源遥感特征数据集。基于融合影像，分别采用最大似然分类、随机森林、支持向量机、面向对象及神经网络方法开展烟草种植区提取，并对不同分类方法的精度表现进行对比分析。结果表明：Sentinel-1/2融合影像能够有效削弱云雾与地物破碎对分类结果的影响，显著提升烟草种植区识别精度。在融合数据条件下，神经网络与支持向量机方法的精度提升最为明显，其中神经网络方法表现最优，其生产精度为95.10%，总体精度由97.32%提升至98.96%,Kappa系数由0.953提升至0.972，用户精度由85.61%提升至97.22%。以金沙县政府公布的年度烟草种植面积统计数据作为区域尺度参考，融合分类结果的面积差异为4.92%。研究表明，基于HSV-PCA方法融合的Sentinel-1/2影像能够实现喀斯特山区烟草种植区的高精度提取。更多还原

【Abstract】 Guizhou Province is one of China’s major tobacco-producing regions; however, in its karst mountainous areas, persistent cloud cover and fragmented land surfaces significantly limit the accuracy of tobacco cultivation mapping using remote sensing. To address this issue, Jinsha County of Bijie City, Guizhou Province, was selected as the study area. Based on the Google Earth Engine platform, Sentinel-1 synthetic aperture radar imagery and Sentinel-2 multispectral data were fused using the HSV-PCA method to construct a multi-source remote sensing feature dataset. Tobacco cultivation areas were extracted using Random Forest, Maximum Likelihood Classification, Support Vector Machine, Neural Network, and Object-Oriented methods, and their classification accuracies were comparatively evaluated. The results indicate that Sentinel-1/2 data fusion effectively mitigates the influence of cloud interference and surface fragmentation, leading to a substantial improvement in classification accuracy. Under the fused data condition, the Neural Network method achieved the best performance, with a producer’s accuracy of 95.10%, an overall accuracy of 98.96%, a Kappa coefficient of 0.972, and a user’s accuracy of 97.22%. Compared with official annual tobacco planting statistics for Jinsha County, the area difference of the fused classification result was 4.92%. These results demonstrate that HSV-PCA based fusion of Sentinel-1 and Sentinel-2 imagery enables high-accuracy extraction of tobacco cultivation areas in karst mountainous regions.更多还原