MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

TL;DR

This paper introduces MARTA GANs, an unsupervised learning model that leverages generative adversarial networks to improve remote sensing image classification using only unlabeled data.

Contribution

It proposes a novel unsupervised GAN-based framework with feature fusion for remote sensing image representation learning, outperforming existing methods.

Findings

Significant improvement in classification accuracy on remote sensing datasets.

Effective use of unlabeled data for feature learning.

Enhanced representation quality through feature fusion.

Abstract

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.