Bag-of-Words for Transfer Learning

Abstract

Although the number of labeled datasets in Earth Observation (EO) is increasing, there is still a major gap between the Deep Learning (DL) classifiers designed in this field versus the models in Computer Vision. This gap is produced mainly by the number of datasets available, but also by the diversity of data. In EO, there are different sensors acquiring images, from multispectral (MS) or hyperspectral data, to SAR imagery. In this paper, we want to demonstrate how to reduce the divergence created by the diversity of data. We trained several DL architectures on Bag-of-Words from large-scale MS and SAR datasets, and then we used transfer learning on smaller ones and evaluated the results. With this method, we demonstrate that a DL architecture can be trained with any type of large-scale data, transformed into Bag-of-Words, and the trained model can be used further on other types of data, without regard on the number of channels.