Abstract:
Most of Earth is covered by haze or clouds, impeding the constant monitoring of our planet. Preceding works have documented the detrimental effects of cloud coverage on r...Show MoreMetadata
Abstract:
Most of Earth is covered by haze or clouds, impeding the constant monitoring of our planet. Preceding works have documented the detrimental effects of cloud coverage on remote sensing applications and proposed ways to approach this issue. However, up to now, little effort has been spent on understanding how exactly atmospheric disturbances impede the application of modern machine learning methods to Earth observation data. Specifically, we consider the effects of haze and cloud coverage on a scene classification task. We provide a thorough investigation of how classifiers trained on cloud-free data fail once they encounter noisy imagery—a common scenario encountered when deploying pretrained models for remote sensing to real use cases. We show how and why remote sensing scene classification suffers from cloud coverage. Based on a multistage analysis, including explainability approaches applied to the predictions, we work out four different types of effects that clouds have on scene prediction. The contribution of our work is to deepen the understanding of the effects of clouds on common remote sensing applications and consequently guide the development of more robust methods.
Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 15)
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- IEEE Keywords
- Clouds ,
- Cloud computing ,
- Remote sensing ,
- Task analysis ,
- Earth ,
- Wetlands ,
- Forestry
- Index Terms
- Remote Sensing ,
- Scene Classification ,
- Cloud Effects ,
- Remote Sensing Scene Classification ,
- Classification Task ,
- Real Use Case ,
- Neural Network ,
- Training Data ,
- Data Distribution ,
- Classification Performance ,
- Network Training ,
- Confusion Matrix ,
- Focus Of Attention ,
- Decrease In Performance ,
- Network Output ,
- Attention Network ,
- Part Of The Image ,
- Probability Vector ,
- Prediction Confidence ,
- False Predictions ,
- Presence Of Clouds ,
- Saliency Map ,
- Accuracy Drop ,
- Small Clouds ,
- Cloud Types ,
- Logit Values ,
- Test Split ,
- Cloud Shadow ,
- Sentinel-2 Data ,
- Mutual Information
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Clouds ,
- Cloud computing ,
- Remote sensing ,
- Task analysis ,
- Earth ,
- Wetlands ,
- Forestry
- Index Terms
- Remote Sensing ,
- Scene Classification ,
- Cloud Effects ,
- Remote Sensing Scene Classification ,
- Classification Task ,
- Real Use Case ,
- Neural Network ,
- Training Data ,
- Data Distribution ,
- Classification Performance ,
- Network Training ,
- Confusion Matrix ,
- Focus Of Attention ,
- Decrease In Performance ,
- Network Output ,
- Attention Network ,
- Part Of The Image ,
- Probability Vector ,
- Prediction Confidence ,
- False Predictions ,
- Presence Of Clouds ,
- Saliency Map ,
- Accuracy Drop ,
- Small Clouds ,
- Cloud Types ,
- Logit Values ,
- Test Split ,
- Cloud Shadow ,
- Sentinel-2 Data ,
- Mutual Information
- Author Keywords