Mobile Hyperspectral Imaging for Material Surface Damage Detection-Reference-Cited by-同舟云学术

Mobile Hyperspectral Imaging for Material Surface Damage Detection

Published:2021-01 Issue:1 Volume:35 Page:04020057
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Aryal Sameer¹,Chen ZhiQiang²^ORCID,Tang Shimin³

Affiliation:

1. Graduate Student, Dept. of Civil and Mechanical Engineering, Univ. of Missouri Kansas City, FH 5110 Rockhill Rd., Kansas City, MO 64110.

2. Associate Professor, School of Computing and Engineering, Univ. of Missouri Kansas City, FH 5110 Rockhill Rd., Kansas City, MO 64110 (corresponding author). ORCID: .

3. Ph.D. Candidate, Dept. of Computer Science and Electrical Engineering, Univ. of Missouri Kansas City, FH 5110 Rockhill Rd., Kansas City, MO 64110.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000934

Reference73 articles.

1. Adler-Golden S. A. Berk L. Bernstein S. Richtsmeier P. Acharya M. Matthew G. Anderson C. Allred L. Jeong and J. Chetwynd. 1998. “FLAASH a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations.” In Proc. 7th JPL Airborne Earth Science Workshop 9–14. Pasadena CA: California Institute of Technology.

2. Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks

3. Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis

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