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Spectral feature extraction and ensemble learning for multiclass aircraft damage identification.

Nilima Zade¹, Aditya Gupte², Pranshu Gupta¹

¹Symbiosis Institute of Technology - Pune Campus, Symbiosis International (Deemed University), Pune, India.

|October 6, 2025

Summary

This study introduces a hyperspectral imaging method for detecting aircraft surface damage. Combining ensemble machine learning models, it accurately identifies ten damage types, improving aerospace maintenance inspection.

Keywords:

Aircraft damage detection Damage Type Identification Ensemble Learning Feature extraction Hyperspectral imaging Non-destructive testing Spectral analysis

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Area of Science:

Aerospace Engineering
Computer Science
Materials Science

Background:

Conventional aircraft surface damage inspection methods are often limited in detecting subtle degradation.
Various forms of surface damage, including corrosion, burn marks, and paint peeling, pose significant safety risks.
Advanced imaging and machine learning offer potential for more effective detection.

Purpose of the Study:

To develop and validate a robust methodology for identifying diverse aircraft surface damage types.
To leverage hyperspectral imaging and ensemble machine learning for enhanced damage classification.
To create a scalable, non-contact inspection solution for aerospace maintenance.

Main Methods:

Collected hyperspectral intensity data from over 500 real and lab-induced aircraft surface samples.
Engineered handcrafted features across spectral, statistical, and frequency domains.
Developed a soft voting ensemble model combining Random Forest, XGBoost, and SVM classifiers.

Main Results:

Achieved a peak classification accuracy of 92.6% for ten different aircraft surface damage types.
Demonstrated high accuracy across various damage classes, outperforming conventional techniques.
Validated the effectiveness of the ensemble model in classifying complex surface degradation patterns.

Conclusions:

The hyperspectral imaging and ensemble machine learning pipeline provides an accurate and efficient method for aircraft surface damage identification.
The developed system is suitable for real-time, non-contact, and scalable inspection workflows.
This methodology shows strong potential for integration into automated drone-based or robotic inspection systems for aerospace maintenance.