发表机构
SRM Institute of Science & Technology（SRM科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对标签稀缺下小规模分散火灾的烧毁农田制图难题，提出校准弱监督框架，利用多源遥感数据与伪标签，实现地区级评估与热点筛查，但像素级精度有限。
AI 中文摘要
当火灾规模小且分散、可靠标签稀缺时，收获后烧毁农田的制图十分困难。我们针对印度旁遮普邦开发了一个校准的弱监督框架，利用 Sentinel-2 光谱变化、VIIRS 活跃火点背景以及 MODIS MCD64A1 作为粗略的外部校准和一致性参考。我们评估了三种伪标签配方、五种特征表示以及线性、基于树、提升和神经网络分类器，采用嵌套的按地区留出交叉验证，跨越三个随机种子和五折。NBR 和 dNBR 被排除在分类器输入之外。最佳配置使用了非常严格配方、多层感知机和完整光学特征集（平均 Cohen's kappa 0.395，AUROC 0.753，F1 0.747，平衡准确率 0.703）；随机森林、XGBoost 和 LightGBM 几乎持平。与留出伪标签的更高一致性并未确立更好的标签正确性或独立的烧毁面积准确性。对于部署，保留了使用非常严格配方和完整光学特征的随机森林。外部校准阈值 0.60 产生了地区级 MODIS 一致性，R 平方为 0.636，制图与 MODIS 烧毁面积之比为 1.005，与地区火灾次数的 Spearman 相关性为 0.779。像素级 MODIS 一致性仍然有限（F1 0.205，kappa 0.093）。向哈里亚纳邦的零样本迁移很有前景（平均 kappa 0.641），但旁遮普邦的跨年稳定性较弱，且 Sentinel-1/Sentinel-2 特征拼接未改善光学基线。光学观测在季节性火灾背景之前结束，限制了对晚期火灾的覆盖。该框架支持地区级负担评估和热点筛查，但对精确烧伤边界或时间稳定的年度制图支持有限。
英文摘要
Mapping post-harvest burned cropland is difficult when fires are small and fragmented and reliable labels are scarce. We developed a calibrated weak-supervision framework for Punjab, India, using Sentinel-2 spectral change, VIIRS active-fire context, and MODIS MCD64A1 as a coarse external calibration and agreement reference. Three pseudo-label recipes, five feature representations, and linear, tree-based, boosted, and neural classifiers were evaluated using nested district-held-out cross-validation over three seeds and five folds. NBR and dNBR were excluded from classifier inputs. The best configuration used the very-strict recipe, a multilayer perceptron, and the full optical feature set (mean Cohen's kappa 0.395, AUROC 0.753, F1 0.747, balanced accuracy 0.703); Random Forest, XGBoost, and LightGBM were practically tied. Higher agreement with held-out pseudo-labels did not establish improved label correctness or independent burned-area accuracy. For deployment, a Random Forest with the very-strict recipe and full optical features was retained. An externally calibrated threshold of 0.60 yielded district-level MODIS agreement of R-squared 0.636, a mapped-to-MODIS burned-area ratio of 1.005, and Spearman correlation of 0.779 with district fire counts. Pixel-level MODIS agreement remained modest (F1 0.205, kappa 0.093). Zero-shot transfer to Haryana was promising (mean kappa 0.641), but Punjab cross-year stability was weak, and Sentinel-1/Sentinel-2 feature concatenation did not improve the optical baseline. Optical observations ended before the seasonal fire context, limiting coverage of late burns. The framework supports district-scale burden assessment and hotspot screening, with limited support for exact scar boundaries or temporally stable annual mapping.
Comments11 pages, 5 figures, 5 tables
