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:
Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand
Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand
Department of Communication and Computer Engineering, Tokyo Institute of Technology, Tokyo, Japan
National Electronics and Computer Technology Center (NECTEC), Pathumthani, Thailand
Digital Economy Agency, Bangkok, Thailand
Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand
Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand
Department of Communication and Computer Engineering, Tokyo Institute of Technology, Tokyo, Japan
National Electronics and Computer Technology Center (NECTEC), Pathumthani, Thailand
Digital Economy Agency, Bangkok, Thailand