Domain Adaptation for Convolutional Neural Networks-Based Remote Sensing Scene Classification

Abstract

Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the RS techniques, a large number of RS scene images are available. As manually labeling large-scale RS scene images is both labor and time consuming, when a new unlabeled data set is obtained, how to use the existing labeled data sets to classify the new unlabeled images is an important research direction. Different RS scene image data sets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions, and image scales, so the distribution discrepancy exists among different image data sets. As a result, simply applying convolutional neural networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain adaptation (DA) can be helpful to solve this problem. In this letter, we design a subspace alignment (SA) and CNN-based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Fine-tuning the modified CNN model with the added SA layer makes the CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public data sets show that adding the SA layer into CNN improves the scene classification on the target domain.