# Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data

Source: [Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data](<https://www.sota2.com/research/paper/68fa63bd1e209131cea008ea>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:34.578Z

Authors: Dimitrios Marmanis, Wei Yao, Fathalrahman Adam, Mihai Datcu, Peter Reinartz, Konrad Schindler, Jan Dirk Wegner, Uwe Stilla

Published: 2017-11-06T16:52:51.000Z

## Abstract

Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field. In this work, we propose such a dataset and use it to explore patch-based classification in urban and periurban areas, considering 7 distinct semantic classes. In this context, we investigate the accuracy of large CNN classification models and pre-trained networks for SAR imaging systems. Furthermore, we propose a Generative Adversarial Network (GAN) for SAR image generation and test, whether the synthetic data can actually improve classification accuracy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1711.02010>)

[PDF](<https://arxiv.org/pdf/1711.02010.pdf>)