Deep Learning for Multisensor Image Resolution Enhancement
| Title | Deep Learning for Multisensor Image Resolution Enhancement |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Collins, CB, Beck, JM, Bridges, SM, Rushing, J, Graves, S |
| Conference Name | GeoAI’17: 1st ACM SIGSPATIAL Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery |
| Date Published | 11/2017 |
| Publisher | ACM |
| Conference Location | Redondo Beach, CA USA |
| ISBN Number | 978-1-4503-5498 |
| Keywords | Deep Learning, Multispectral Imagery, Super Image Resolution |
| Abstract | We describe a deep learning convolutional neural network (CNN) for enhancing low resolution multispectral satellite imagery without the use of a panchromatic image. For training, low resolution images are used as input and corresponding high resolution images are used as the target output (label). The CNN learns to automatically extract hierarchical features that can be used to enhance low resolution imagery. The trained network can then be effectively used for super-resolution enhancement of low resolution multispectral images where no corresponding high resolution image is available. The CNN enhances all four spectral bands of the low resolution image simultaneously and adjusts pixel values of the low resolution to match the dynamic range of the high resolution image. The CNN yields higher quality images than standard image resampling methods. |
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