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.

本研究探讨了遥感图像中的目标存在检测和定位问题，主要关注太阳能电池板的识别。我们评估了三种模型的不同监督水平，分别为完全监督的目标检测器、基于 CAM 的弱监督图像分类器与定位器以及最小监督异常检测器。分类器在二值目标存在检测方面表现出色（0.79 的 F1 得分），而目标检测器则提供了精确的定位（0.72 的得分）。异常检测器需要更多数据以获得可行的性能。模型结果的融合显示出潜在的准确度提升。CAM 对定位的影响有限，GradCAM、GradCAM++ 和 HiResCAM 得到了更好的结果。值得注意的是，与目标检测器相比，分类器在数据较少的情况下仍然表现出鲁棒性。