This study investigates object presence detection and localization in remote sensing imagery, focusing on solar panel recognition. We explore different levels of supervision, evaluating three models: a fully supervised object detector, a weakly supervised image classifier with CAM-based localization, and a minimally supervised anomaly detector. The classifier excels in binary presence detection (0.79 F1-score), while the object detector (0.72) offers precise localization. The anomaly detector requires more data for viable performance. Fusion of model results shows potential accuracy gains. CAM impacts localization modestly, with GradCAM, GradCAM++, and HiResCAM yielding superior results. Notably, the classifier remains robust with less data, in contrast to the object detector.

翻译：本研究探讨遥感图像中的目标存在检测与定位问题，重点关注太阳能电池板识别。我们评估了三种不同监督级别的模型：全监督目标检测器、基于CAM的弱监督图像分类器以及最小监督异常检测器。分类器在二值存在检测任务中表现优异（F1分数0.79），而目标检测器（F1分数0.72）能提供精确定位。异常检测器需要更多数据才能达到可行性能。模型结果融合显示出潜在精度提升空间。CAM对定位的影响有限，其中GradCAM、GradCAM++和HiResCAM效果更优。值得注意的是，与目标检测器相比，分类器在数据较少时仍保持稳健性。