Foreign object debris (FOD) on airport runways is an important factor affecting aircraft flight safety, and current FOD detection technologies all have obvious deficiencies. In this paper, an indoor near-infrared (NIR) hyperspectral image data acquisition system with a wavelength range of 900-1700nm was built. The 14 samples of 6 common FODs and airport concrete runways were divided into reference and test sample sets, and the atlas data were collected for two common application scenarios. Preprocessing was performed on the reference sample set of hyperspectral images and reference spectral curves were extracted for 7 types of samples. Six spectral matching algorithms based on spectral angle matching (SAM), spectral information divergence (SID), spectral correlation coefficient (SCC) and their combinations are used to classify pixels one by one. By comparing the classification map, overall accuracy (OA), average accuracy (AA), and Kappa coefficient, a NIR hyperspectral FOD detection method based on SAM-SID (threshold Sc=40 pixel) criterion is obtained. The proposed method obtained ideal classification maps for the test sample set, with OA, AA and Kappa coefficients reaching 92%, 82% and 0.82, respectively, thus achieving good validation.
At present, there are few studies on nondestructive testing of aircraft surface based on hyperspectral imaging at home and abroad. Therefore, an indoor near infrared (NIR) hyperspectral damage detection system with a spectral resolution of 5nm was established, and the paint damage on the sample surface was identified. The reflectance calibration, average reflectance calculation and principal component analysis (PCA) dimensionality reduction were performed on the collected hyperspectral data. On this basis, the unsupervised classification iterative self-organizing Data analysis algorithm (ISODATA) is used to identify the damaged samples. The results show that the spectral curves of the damaged and undamaged pixels of the sample are significantly different at about 910nm. The first 10 principal components selected can contain 97% of the sample data information, which can realize the effective identification of damage samples based on ISODATA. In this study, paint damage was taken as an experimental sample to verify the feasibility of using near-infrared hyperspectral imaging technology for damage identification. In addition, preliminary outfield experiment results also show that it is feasible to apply this technology to aircraft surface damage detection.
Monitoring the formation process and occurrence state of methane in abyssal gas-liquid-hydrate coexistent system is the premise for gas hydrate research and exploitation, and the key lies in real time, synchronous and in-situ acquisition of multi state parameters, like concentration, temperature, pressure of methane. In this paper, we propose a novel multi parameter in situ methane sensor (Submarine Methane Imaging Interference Spectrometer, SMIIS) that can simultaneously measure concentration, temperature and pressure information of submarine methane. Then to evaluate SMIIS’s feasibility and performance, we build SMIIS’s simulation model and analyze its forward interferogram. The signal-to-noise ratios (SNRs) of the simulation interference fringes for the six spectral lines of methane are in the range of (3 - 618). The detection sensitivities for concentration, temperature and pressure measurements can reach to 0.5 nmol/L, 0.5 K, and 0.05 MPa, respectively. The results indicate that the preliminary design of SMIIS is feasible. After further testing and improvement, this system will have the potential to be applied to the seabed methane detection.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.