Award Abstract # 2003740
RUI: Collaborative Research: CDS&E: A Modular Multilayer Framework for Real-Time Hyperspectral Image Segmentation

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: AUBURN UNIVERSITY MONTGOMERY
Initial Amendment Date: July 23, 2020
Latest Amendment Date: May 23, 2022
Award Number: 2003740
Award Instrument: Standard Grant
Program Manager: Sheikh Ghafoor
sghafoor@nsf.gov
 (703)292-7116
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: August 1, 2020
End Date: July 31, 2025 (Estimated)
Total Intended Award Amount: $207,664.00
Total Awarded Amount to Date: $207,664.00
Funds Obligated to Date: FY 2020 = $207,664.00
History of Investigator:
  • OLCAY KURSUN (Principal Investigator)
    okursun@aum.edu
  • Semih Dinc (Former Principal Investigator)
Recipient Sponsored Research Office: Auburn University at Montgomery
7430 EAST DR LIBRARY TOWER 700
MONTGOMERY
AL  US  36117
(334)244-3249
Sponsor Congressional District: 02
Primary Place of Performance: Auburn University at Montgomery
AUM Office of Research & Sponsor
Montgomery
AL  US  36124-4023
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): S2Y7SMVZ1R96
Parent UEI:
NSF Program(s): CDS&E
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 8084, 026Z
Program Element Code(s): 808400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The analysis of images has been used by the scientific community to solve challenging problems and to get insight into diverse natural, social, and technical phenomena. Different types of images have been employed in various areas of study. One example is the hyperspectral images, which have higher resolution when compared to conventional camera images. Analyzing such images has its challenges. For instance, it is computationally demanding, and traditional methods have some limitations. This project provides an efficient solution to analyze such images, by exploiting high-performance computing tools and machine learning techniques. The resulting methods are applied to image-based atmospheric cloud detection.

The project develops a real time, multi-layer, and modular segmentation framework for hyperspectral images. The developed framework automatically identifies various regions within a hyperspectral image by classifying each pixel of the image and associating them to class segments. The developed system is multi-layer, where each layer?s responsibility is to perform an operation on its input, generate region classification data, and pass the resultant output to the next layer. Importantly, each layer analyzes its input from distinct viewpoints, utilizing spectral and spatial data, resulting in a multi-layer framework where the layers complement each other. Also, this project aims to provide an optimized high-performance (speed-up and accuracy) computational tool for real-time hyperspectral image analysis. This is achieved by adapting the algorithms used in the different parts of the model for parallel processing.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Bellio, Giovanni and Russell, Randy and Kursun, Olcay "Boosting With Multiple Clustering Memberships For Hyperspectral Image Classification" SoutheastCon 2023 , 2023 https://doi.org/10.1109/SoutheastCon51012.2023.10115209 Citation Details
Dinc, Semih and Russell, Randy and Parra, Luis Alberto "Cloud Region Segmentation from All Sky Images using Double K-Means Clustering" 2022 IEEE International Symposium on Multimedia (ISM) , 2022 https://doi.org/10.1109/ISM55400.2022.00058 Citation Details
Kursun, Olcay and Dinc, Semih and Favorov, Oleg V. "Contextually Guided Convolutional Neural Networks for Learning Most Transferable Representations" 2022 IEEE International Symposium on Multimedia (ISM) , 2022 https://doi.org/10.1109/ISM55400.2022.00047 Citation Details
Kursun, Olcay and Favorov, Oleg V "Sinbad Origins Of Contextually-Guided Feature Learning: Self-Supervision With Local Context For Target Detection" , 2024 https://doi.org/10.1109/SoutheastCon52093.2024.10500116 Citation Details
Kursun, Olcay and Nguyen, Hoa T. and Favorov, Oleg V. "Cross-Modal Prediction of Superclasses Using Cortex-Inspired Neural Architecture" 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART) , 2023 https://doi.org/10.1109/BioSMART58455.2023.10162118 Citation Details
Kursun, Olcay and Sarsekeyev, Beiimbet and Hasanzadeh, Mahdi and Patooghy, Ahmad and Favorov, Oleg V "Tactile Sensing with Contextually Guided CNNs: A Semisupervised Approach for Texture Classification" , 2023 https://doi.org/10.1109/IRC59093.2023.00011 Citation Details

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