Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning
This addresses the high cost of labeled data collection for remote sensing applications, though it is incremental as it applies existing self-supervised techniques to a specific domain.
The paper tackles the insufficient labeled data problem in satellite image classification by using self-supervised learning to create synthetic labels from raw satellite images, showing that models trained on these synthetic labels achieve similar performance to those trained on real labels.
Supervised deep neural networks are the-state-of-the-art for many tasks in the remote sensing domain, against the fact that such techniques require the dataset consisting of pairs of input and label, which are rare and expensive to collect in term of both manpower and resources. On the other hand, there are abundance of raw satellite images available both for commercial and academic purposes. Hence, in this work, we tackle the insufficient labeled data problem in satellite image classification task by introducing the process based on the self-supervised learning technique to create the synthetic labels for satellite image patches. These synthetic labels can be used as the training dataset for the existing supervised learning techniques. In our experiments, we show that the models trained on the synthetic labels give similar performance to the models trained on the real labels. And in the process of creating the synthetic labels, we also obtain the visual representation vectors that are versatile and knowledge transferable.