Next Article in Journal
Monitoring Severe Slugging in Pipeline-Riser System Using Accelerometers for Application in Early Recognition
Previous Article in Journal
Development of Fluorescent Reagent Based on Ligand Exchange Reaction for the Highly Sensitive and Selective Detection of Dopamine in the Serum
Previous Article in Special Issue
CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems
Open AccessReview

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

1
Signal Processing Lab (SPL), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Crete, Greece
2
Computer Science Department, University of Crete, 70013 Crete, Greece
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(18), 3929; https://doi.org/10.3390/s19183929
Received: 25 July 2019 / Revised: 4 September 2019 / Accepted: 9 September 2019 / Published: 12 September 2019
(This article belongs to the Special Issue Deep Learning Remote Sensing Data)
Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed. View Full-Text
Keywords: deep learning; convolutional neural networks; generative adversarial networks; super-resolution; denoising; pan-sharpening; fusion; earth observations; satellite imaging deep learning; convolutional neural networks; generative adversarial networks; super-resolution; denoising; pan-sharpening; fusion; earth observations; satellite imaging
Show Figures

Figure 1

MDPI and ACS Style

Tsagkatakis, G.; Aidini, A.; Fotiadou, K.; Giannopoulos, M.; Pentari, A.; Tsakalides, P. Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement. Sensors 2019, 19, 3929.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop