dc.contributor.author |
Faniso-Mnyaka, Zimbini
|
|
dc.contributor.author |
Skosana, Vusi J
|
|
dc.contributor.author |
Nana, Muhammad A
|
|
dc.contributor.author |
Magidimisha, Edwin
|
|
dc.date.accessioned |
2023-03-17T09:54:46Z |
|
dc.date.available |
2023-03-17T09:54:46Z |
|
dc.date.issued |
2022-10 |
|
dc.identifier.citation |
Faniso-Mnyaka, Z., Skosana, V.J., Nana, M.A. & Magidimisha, E. 2022. Image quality assessment methods for near-infrared wildfire imagery. http://hdl.handle.net/10204/12683 . |
en_ZA |
dc.identifier.issn |
2831-3682 |
|
dc.identifier.uri |
DOI: 10.1109/ICEET56468.2022.10007146
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/12683
|
|
dc.description.abstract |
Over the past two decades, there has been a surge of interest in the study of image quality assessment due to its broad applicability in many fields. Satellites and other remote sensing applications have been collecting vital data that is utilised to monitor targets or events in varying environmental conditions all over the world. Some of these collections include images of natural disasters and anthropogenic events such as wildfires, floods, and drought, among others. However, appropriate image quality assessment techniques have been lacking for image fusion and other remote sensing applications where the information is not targeting the human visual system. Currently, there are several perceptual image quality assessment methods that can be applied depending on the image sensor type. In this paper, we focus on various no-reference general and specific image quality methods that can be used to evaluate remote sensing images for fire detection. Further, we evaluate the effectiveness of the non-referential image quality techniques applied in the processing of airborne sensor images, notably those for fire detection, and correlate the effectiveness of these techniques to the accuracy of detection. In this paper Image quality assessment (IQA) methods such as entropy, BRISQUE, MUSIQ, exposure, and CPBD are analyzed along with methods for image distortion, i.e., Gaussian blur, and image enhancement such as HE, AHE, and CLAHE. Therefore, the no-reference image quality assessment investigation will contribute to the detection and correction of image quality processing issues in wildfires. |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://iceet.net/wp-content/uploads/2022/10/Program_ICEET_tv5.pdf |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/10007146 |
en_US |
dc.source |
Proceedings of the 8th International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia, 27-28 October 2022 |
en_US |
dc.subject |
No-reference method |
en_US |
dc.subject |
Image quality |
en_US |
dc.subject |
Remote sensing |
en_US |
dc.subject |
Image distortions |
en_US |
dc.subject |
Wildfires |
en_US |
dc.subject |
Image enhancements |
en_US |
dc.title |
Image quality assessment methods for near-infrared wildfire imagery |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
6 |
en_US |
dc.description.note |
©2022 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: DOI: 10.1109/ICEET56468.2022.10007146 |
en_US |
dc.description.cluster |
Defence and Security |
en_US |
dc.description.impactarea |
Optronic Sensor Systems |
en_US |
dc.identifier.apacitation |
Faniso-Mnyaka, Z., Skosana, V. J., Nana, M. A., & Magidimisha, E. (2022). Image quality assessment methods for near-infrared wildfire imagery. http://hdl.handle.net/10204/12683 |
en_ZA |
dc.identifier.chicagocitation |
Faniso-Mnyaka, Zimbini, Vusi J Skosana, Muhammad A Nana, and Edwin Magidimisha. "Image quality assessment methods for near-infrared wildfire imagery." <i>Proceedings of the 8th International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia, 27-28 October 2022</i> (2022): http://hdl.handle.net/10204/12683 |
en_ZA |
dc.identifier.vancouvercitation |
Faniso-Mnyaka Z, Skosana VJ, Nana MA, Magidimisha E, Image quality assessment methods for near-infrared wildfire imagery; 2022. http://hdl.handle.net/10204/12683 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Faniso-Mnyaka, Zimbini
AU - Skosana, Vusi J
AU - Nana, Muhammad A
AU - Magidimisha, Edwin
AB - Over the past two decades, there has been a surge of interest in the study of image quality assessment due to its broad applicability in many fields. Satellites and other remote sensing applications have been collecting vital data that is utilised to monitor targets or events in varying environmental conditions all over the world. Some of these collections include images of natural disasters and anthropogenic events such as wildfires, floods, and drought, among others. However, appropriate image quality assessment techniques have been lacking for image fusion and other remote sensing applications where the information is not targeting the human visual system. Currently, there are several perceptual image quality assessment methods that can be applied depending on the image sensor type. In this paper, we focus on various no-reference general and specific image quality methods that can be used to evaluate remote sensing images for fire detection. Further, we evaluate the effectiveness of the non-referential image quality techniques applied in the processing of airborne sensor images, notably those for fire detection, and correlate the effectiveness of these techniques to the accuracy of detection. In this paper Image quality assessment (IQA) methods such as entropy, BRISQUE, MUSIQ, exposure, and CPBD are analyzed along with methods for image distortion, i.e., Gaussian blur, and image enhancement such as HE, AHE, and CLAHE. Therefore, the no-reference image quality assessment investigation will contribute to the detection and correction of image quality processing issues in wildfires.
DA - 2022-10
DB - ResearchSpace
DP - CSIR
J1 - Proceedings of the 8th International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia, 27-28 October 2022
KW - No-reference method
KW - Image quality
KW - Remote sensing
KW - Image distortions
KW - Wildfires
KW - Image enhancements
LK - https://researchspace.csir.co.za
PY - 2022
SM - 2831-3682
T1 - Image quality assessment methods for near-infrared wildfire imagery
TI - Image quality assessment methods for near-infrared wildfire imagery
UR - http://hdl.handle.net/10204/12683
ER -
|
en_ZA |
dc.identifier.worklist |
26018 |
en_US |