Remote sensing for petroleum exploration, Part 1: Overview of imaging systems

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.

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NEW STANDARDS FOR FEVER SCREENING WITH THERMAL IMAGING SYSTEMS

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519413500450 ◽

2013 ◽

Vol 13 (02) ◽

pp. 1350045 ◽

Cited By ~ 9

Author(s):

E. F. J. RING ◽

A. JUNG ◽

B. KALICKI ◽

J. ZUBER ◽

A. RUSTECKA ◽

...

Keyword(s):

Remote Sensing ◽

Body Temperature ◽

Human Body ◽

Thermal Imaging ◽

Imaging Systems ◽

Contact Site ◽

Infrared Thermal Imaging ◽

Significant Difference ◽

Human Body Temperature ◽

Eye Region

Infrared thermal imaging has in recent years become more accessible and affordable as a means of remote sensing for human body temperature such as in identifying a person with fever. The implementation and operational guidelines for identifying a febrile human using a screening thermograph as documented in the ISO/TR 13154:2009 ISO/TR 80600 has been deployed for the screening of a total of 402 children. It was found that there was a significant difference between the temperatures measured in non-fevered patients and those with known fever, with the thermal imaging of the eye region being the most rapid non-contact site for measurement.

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