This work proposes a hybrid unsupervised/supervised learning method to pretrain models applied in earth observation downstream tasks where only a handful of labels denoting very general semantic concepts are available. We combine a contrastive approach to pretrain models with a pretext task to predict spatially coarse elevation maps which are commonly available worldwide. The intuition behind is that there is generally some correlation between the elevation and targets in many remote sensing tasks, allowing the model to pre-learn useful representations. We assess the performance of our approach on a segmentation downstream task on labels gathering many possible subclasses (pixel level classification of farmlands vs. other) and an image binary classification task derived from the former, on a dataset on the north-east of Colombia. On both cases we pretrain our models with 39K unlabeled images, fine tune the downstream task only with 80 labeled images and test it with 2944 labeled images. Our experiments show that our methods, GLCNet+Elevation for segmentation and SimCLR+Elevation for classification, outperform their counterparts without the elevation pretext task in terms of accuracy and macro-average F1, which supports the notion that including additional information correlated to targets in downstream tasks can lead to improved performance.

该研究提出一种混合无监督/监督学习方法，结合反差预训练模型和预测粗略海拔图像的预文本任务，以处理只有少量标签的地球观测下游任务，并评估其在分割和图像分类任务中的实验结果。

一种基于高程数据的对比方法，用于稀缺和高级语义标签的遥感分类