In-domain representation learning for remote sensing
arXiv:1911.06721
Abstract
Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to establish baselines and a common evaluation protocol in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form. Specifically, we investigate in-domain representation learning to develop generic remote sensing representations and explore which characteristics are important for a dataset to be a good source for remote sensing representation learning. The established baselines achieve state-of-the-art performance on these datasets.
References in corpus (10)
- Conditional Generative Adversarial Nets
- How transferable are features in deep neural networks?
- OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
- Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
- CNN Features off-the-shelf: an Astounding Baseline for Recognition
- BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding
- Land Use Classification in Remote Sensing Images by Convolutional Neural Networks
- MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification
- Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
- Domain Adaptive Transfer Learning with Specialist Models