Abstract:
The uncontrollable weather conditions can cause a serious problem to remote sensing imaginary. One of the weather conditions is a resulting from cloud contamination. As a...Show MoreMetadata
Abstract:
The uncontrollable weather conditions can cause a serious problem to remote sensing imaginary. One of the weather conditions is a resulting from cloud contamination. As a result, this paper proposed the use of the convolutional autoencoder neural networks to remove clouds from cloud-contaminated images by training on a multi-temporal remote sensing dataset. Here, the observations from different spectral bands are assumed to be independent since their spectral responses are usually nonoverlapped. From this assumption, each convolutional autoencoder neural networks are trained with the observation from only one spectral band. In our method, we have three convolutional autoencoder neural networks for red, green and blue spectral bands. The experiments were conducted on both synthesis and real dataset derived from the actual LANDSAT 8 images from the central part of Thailand where our algorithm has shown to have a superb performance.
Date of Conference: 18-21 July 2018
Date Added to IEEE Xplore: 20 January 2019
ISBN Information:
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- IEEE Keywords
- Index Terms
- Neural Network ,
- Convolutional Neural Network ,
- Remote Sensing ,
- Remote Sensing Images ,
- Convolutional Autoencoder ,
- Cloud Removal ,
- Convolutional Autoencoder Neural Network ,
- Spectral Bands ,
- Blue Band ,
- Use Of Neural Networks ,
- Cloud Contamination ,
- Superb Performance ,
- Convolutional Network ,
- Satellite Images ,
- Generative Adversarial Networks ,
- Peak Signal-to-noise Ratio ,
- Skip Connections ,
- Markov Random Field ,
- Autoencoder Network ,
- Operational Land Imager ,
- Structural Similarity Index Measure ,
- Cloud-free Images ,
- Multi-temporal Data
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Convolutional Neural Network ,
- Remote Sensing ,
- Remote Sensing Images ,
- Convolutional Autoencoder ,
- Cloud Removal ,
- Convolutional Autoencoder Neural Network ,
- Spectral Bands ,
- Blue Band ,
- Use Of Neural Networks ,
- Cloud Contamination ,
- Superb Performance ,
- Convolutional Network ,
- Satellite Images ,
- Generative Adversarial Networks ,
- Peak Signal-to-noise Ratio ,
- Skip Connections ,
- Markov Random Field ,
- Autoencoder Network ,
- Operational Land Imager ,
- Structural Similarity Index Measure ,
- Cloud-free Images ,
- Multi-temporal Data
- Author Keywords