SUPER-RESOLUTION OF MULTISPECTRAL SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
| Autor: | Nikoo Ekhtiari, Markus U. Müller, Rodrigo M. Almeida, Christoph Rieke |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Applied optics. Photonics
FOS: Computer and information sciences 010504 meteorology & atmospheric sciences Computer science Computer Vision and Pattern Recognition (cs.CV) Multispectral image Computer Science - Computer Vision and Pattern Recognition 0211 other engineering and technologies 68-06 02 engineering and technology lcsh:Technology 01 natural sciences Convolutional neural network FOS: Electrical engineering electronic engineering information engineering Satellite imagery Computer vision Image resolution 021101 geological & geomatics engineering 0105 earth and related environmental sciences I.4.3 lcsh:T business.industry Deep learning Image and Video Processing (eess.IV) lcsh:TA1501-1820 Electrical Engineering and Systems Science - Image and Video Processing Panchromatic film lcsh:TA1-2040 Computer Science::Computer Vision and Pattern Recognition RGB color model Satellite Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-1-2020, Pp 33-40 (2020) |
| ISSN: | 2194-9050 |
| DOI: | 10.5194/isprs-annals-v-1-2020-33-2020 |
| Popis: | Super-resolution aims at increasing image resolution by algorithmic means and has progressed over the recent years due to advances in the fields of computer vision and deep learning. Convolutional Neural Networks based on a variety of architectures have been applied to the problem, e.g. autoencoders and residual networks. While most research focuses on the processing of photographs consisting only of RGB color channels, little work can be found concentrating on multi-band, analytic satellite imagery. Satellite images often include a panchromatic band, which has higher spatial resolution but lower spectral resolution than the other bands. In the field of remote sensing, there is a long tradition of applying pan-sharpening to satellite images, i.e. bringing the multispectral bands to the higher spatial resolution by merging them with the panchromatic band. To our knowledge there are so far no approaches to super-resolution which take advantage of the panchromatic band. In this paper we propose a method to train state-of-the-art CNNs using pairs of lower-resolution multispectral and high-resolution pan-sharpened image tiles in order to create super-resolved analytic images. The derived quality metrics show that the method improves information content of the processed images. We compare the results created by four CNN architectures, with RedNet30 performing best. To be published in the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: https://www.isprs.org/publications/annals.aspx, proceedings of the XXIV ISPRS Congress, 14-20 June 2020, Nice, France |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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