# MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

arXiv: https://arxiv.org/abs/1612.08879 · HTML: https://mlsanity.com/paper/1612.08879/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2016.

## Built on by (most cited first, 16 of 16)

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- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks](https://mlsanity.com/paper/2111.10079/) (2021)
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- [Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2302.01793/) (2023)
- [Image Augmentation for Satellite Images](https://mlsanity.com/paper/2207.14580/) (2022)
- [Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study](https://mlsanity.com/paper/2107.07761/) (2021)
- [Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model](https://mlsanity.com/paper/2306.05951/) (2023)
- [Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set](https://mlsanity.com/paper/2105.12893/) (2021)
- [Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models](https://mlsanity.com/paper/2208.02089/) (2022)
- [Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation](https://mlsanity.com/paper/2303.05240/) (2023)
- [RENs: Relevance Encoding Networks](https://mlsanity.com/paper/2205.13061/) (2022)
- [CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2301.13473/) (2023)
- [Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification](https://mlsanity.com/paper/2303.11389/) (2023)
- [GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging](https://mlsanity.com/paper/2410.00173/) (2024)

## Abstract (arXiv)

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.