M. Anwar Ma’Sum, M. Pratama, R. Savitha, L. Liu, Habibullah and R. Kowalczyk, "Unsupervised Few-Shot Continual Learning for Remote Sensing Image Scene Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024
Abstract:
A continual learning (CL) model is desired for remote sensing (RS) image analysis because of varying camera parameters, spectral ranges, resolutions, etc. There exist some recent initiatives to develop CL techniques in this domain but they still depend on massive labeled samples which do not fully fit RS applications because ground truths are often obtained via field-based surveys. This article addresses this problem with a proposal of unsupervised flat-wide learning approach (UNISA) for unsupervised few-shot CL approaches of RS image scene classifications which do not depend on any labeled samples for its model updates. UNISA is developed from the idea of prototype scattering and positive sampling for learning representations while the catastrophic forgetting (CF) problem is tackled with the flat-wide learning approach combined with a ball generator to address the data scarcity problem. Our numerical study with RS image scene datasets and a hyperspectral dataset confirms the advantages of our solution. For future studies and reproductions, source codes of UNISA are shared publicly in https://github.com/anwarmaxsum/UNISA.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done