Image Augmentation for Satellite Images
This study proposes the use of generative models (GANs) for augmenting the EuroSAT dataset for the Land Use and Land Cover (LULC) Classification task. We used DCGAN and WGAN-GP to generate images for each class in the dataset. We then explored the effect of augmenting the original dataset by about 10% in each case on model performance. The choice of GAN architecture seems to have no apparent effect on the model performance. However, a combination of geometric augmentation and GAN-generated images improved baseline results. Our study shows that GANs augmentation can improve the generalizability of deep classification models on satellite images.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationImage AugmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite images
With the limited availability of labeled data with various atmospheric conditions in remote sensing images, it seems useful to work with self-supervised algorithms. Few pretext-based algorithms, including from rotation, …
Self-Supervised LearningEvaluating the Efficacy of Cut-and-Paste Data Augmentation in Semantic Segmentation for Satellite Imagery
Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the ad…
Data AugmentationSegmentationSemantic SegmentationSatDepth: A Novel Dataset for Satellite Image Matching
Recent advances in deep-learning based methods for image matching have demonstrated their superiority over traditional algorithms, enabling correspondence estimation in challenging scenes with significant differences in …
An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow
Satellite imagery allows a plethora of applications ranging from weather forecasting to land surveying. The rapid development of computer vision systems could open new horizons to the utilization of satellite data due to…
Data AugmentationDeep LearningGeneral ClassificationImage Augmentation+3Synthetic Data Augmentation for Satellite-Based Analysis of Battle-Damaged Agricultural Fields in Ukraine
Monitoring war-induced damage to agricultural land in Ukraine is important for understanding threats to food security, environmental stability, and post-war recovery. However, the development of computer-vision systems f…
Data Augmentation