Remote sensing image super-resolution: Challenges and approaches

Remote sensing has a growing relevance in the modern society with the development of image processing of satellite imagery. However, due to the limitations of the current imaging sensors and the complex atmospheric conditions, we are facing great challenges in the remote sensing applications due to...

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Bibliographic Details
Published in2015 IEEE International Conference on Digital Signal Processing (DSP) pp. 196 - 200
Main Authors Yang, Daiqin, Li, Zimeng, Xia, Yatong, Chen, Zhenzhong
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 09.09.2015
Subjects
Categories
Conferences
Digital signal processing
Image enhancement
Image reconstruction
Image resolution
Interpolation
observation model
Remote sensing
Signal resolution
Spatial resolution
Spectra
Super resolution
Wavelet transforms
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Summary:Remote sensing has a growing relevance in the modern society with the development of image processing of satellite imagery. However, due to the limitations of the current imaging sensors and the complex atmospheric conditions, we are facing great challenges in the remote sensing applications due to the limited spatial, spectral, radiometric and temporal resolutions. Therefore, super-resolution techniques have attracted much attention by which the low quality low resolution remote sensing images are enhanced. In this paper, we discuss the challenges in remote sensing image super-resolution and thereafter review the relevant approaches. More specifically, the different categories of remote sensing techniques, i.e., the learning-based, interpolation based, frequency domain based, and probability based methods, are reviewed and discussed. Furthermore, the super-resolution applications are discussed and insightful comments on future research directions are provided.
Bibliography:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:1546-1874
2165-3577
DOI:10.1109/ICDSP.2015.7251858