A new public Alsat-2B dataset for single-image super-resolution
This provides a dataset for remote sensing researchers to benchmark super-resolution methods, but it is incremental as it focuses on data creation rather than novel algorithmic advances.
The paper tackled the lack of reliable training datasets for remote sensing image super-resolution by introducing a new public dataset (Alsat-2B) with low and high spatial resolution images (10m and 2.5m), and assessed existing methods on it, revealing promising results but highlighting challenges that require advanced methods.
Currently, when reliable training datasets are available, deep learning methods dominate the proposed solutions for image super-resolution. However, for remote sensing benchmarks, it is very expensive to obtain high spatial resolution images. Most of the super-resolution methods use down-sampling techniques to simulate low and high spatial resolution pairs and construct the training samples. To solve this issue, the paper introduces a novel public remote sensing dataset (Alsat2B) of low and high spatial resolution images (10m and 2.5m respectively) for the single-image super-resolution task. The high-resolution images are obtained through pan-sharpening. Besides, the performance of some super-resolution methods on the dataset is assessed based on common criteria. The obtained results reveal that the proposed scheme is promising and highlight the challenges in the dataset which shows the need for advanced methods to grasp the relationship between the low and high-resolution patches.