Revisiting pre-trained remote sensing model benchmarks: resizing and
  normalization matters
Isaac Corley Caleb Robinson Rahul Dodhia Juan M. Lavista Ferres Peyman Najafirad
Research in self-supervised learning (SSL) with natural images has progressed
rapidly in recent years and is now increasingly being applied to and
benchmarked with datasets containing remotely sensed imagery. A common
benchmark case is to evaluate SSL pre-trained model embeddings on datasets of
remotely sensed imagery with small patch sizes, e.g., 32x32 pixels, whereas
standard SSL pre-training takes place with larger patch sizes, e.g., 224x224.
Furthermore, pre-training methods tend to use different image normalization
preprocessing steps depending on the dataset. In this paper, we show, across
seven satellite and aerial imagery datasets of varying resolution, that by
simply following the preprocessing steps used in pre-training (precisely, image
sizing and normalization methods), one can achieve significant performance
improvements when evaluating the extracted features on downstream tasks -- an
important detail overlooked in previous work in this space. We show that by
following these steps, ImageNet pre-training remains a competitive baseline for
satellite imagery based transfer learning tasks -- for example we find that
these steps give +32.28 to overall accuracy on the So2Sat random split dataset
and +11.16 on the EuroSAT dataset. Finally, we report comprehensive benchmark
results with a variety of simple baseline methods for each of the seven
datasets, forming an initial benchmark suite for remote sensing imagery.