| 研究実績の概要 |
The purpose of this research is to enhance the quality of remote sensing images using deep 3D convolutional neural networks (CNNs).
Improving the performance of CNNs-based methods with a few parameters and short processing time is very difficult, although it is a desirable task to improve the quality of remote sensing images. Thus, I proposed a new 2D CNN network using a parallel-connected backbone, the architecture of which consists residual connections and channel-attention mechanism. This work has been accepted by ACCV Workshop on Machine Learning and Computing for Visual Semantic Analysis, 2020.
In addition, I proposed a new multi-spectral image fusion method using a combination of the proposed lightweight 3D VolumeNet model (which has been accepted by IEEE Transactions on Image Processing, 2021) and the texture transfer method using other modality high-resolution images. The experimental results show that the proposed method outperforms the existing methods in terms of objective accuracy assessment, efficiency and visual subjective evaluation. Consequently, I plan to submit this work to the IEEE Transactions on Geoscience and Remote Sensing.
Overall, the progress of the research is basically in line with the original plan. I studied and referred to various state-of-the-art methods and then built my original models. It is worth noting that the proposed methods not only can exceed the existing methods in accuracy, but also has a faster processing speed and lower hardware requirements for saving the model, so they are suitable for practical applications.
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