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The developments in hyperspectral technology in different applications are known in many fields particularly in remote sensing, airborne imagery, mineral identification and core logging. The automatic mineral identification system provides considerable assistance in geology to identify mineral automatically. Here, the proposed approach addresses an automated system for mineral (i.e. pyrope, olivine, quartz) identification in the long-wave infrared (7.7–11.8 μm - LWIR) ground-based spectroscopy. A low-rank Sparse Principal Component Analysis (Sparse-PCA) based spectral comparison methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Normalized Cross Correlation (NCC) have been used to extract the features in the form of false colors composite. Low-rank Sparse-PCA is used to extract the spectral reference which and showed high similarity to the ASTER (JPL/NASA) spectral library. For decision making step, two methods used to establish a comparison between a kernel Extreme Learning Machine (ELM) and Principal Component Analysis (PCA) kernel K-means clustering. ELM yields classification accuracy up to 76.69% using SAM based polynomial kernel ELM for pyrope mixture, and 70.95% using SAM based sigmoid kernel ELM for olivine mixture. This accuracy is slightly lower as compared to clustering which yields an identification accuracy of 84.91% (NCC) and 69.9% (SAM). However, the supervised classification significantly depends on the number of training samples and is considerably more difficult as compared to clustering due to labeling and training limitations. Moreover, the results indicate considerable similarity between the spectra from low rank approximation from the spectra of pure sample and the spectra from the ASTER spectral library.

Thermal and infrared imagery creates considerable developments in Non-Destructive Testing (NDT) area. Here, a thermography method for NDT specimens inspection is addressed by applying a technique for computation of eigen-decomposition which refers as Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). The proposed approach uses a shorter computational alternative to estimate covariance matrix and Singular Value Decomposition (SVD) to obtain the result of Principal Component Thermography (PCT) and ultimately segments the defects in the specimens applying color based K-medoids clustering approach. The problem of computational expenses for high-dimensional thermal image acquisition is also investigated. Three types of specimens (CFRP, Plexiglas and Aluminium) have been used for comparative benchmarking. The results conclusively indicate the promising performance and demonstrate a confirmation for the outlined properties.

The experimental results from infrared thermography surveys over two buildings externally exposed walls are presented. Data acquisition was performed on a static configuration by recording direct and indirect solar loading during several days and was processed using advanced signal processing techniques in order to increase signal-to-noise ratio and signature contrast of the elements of interest. It is demonstrated that it is possible to detect the thermal signature of large internal structures as well as surface features under such thermographic scenarios. Results from a long-wave microbolometer compared favorably to those from a mid-wave cooled infrared camera for the detection of large subsurface features from unprocessed images. In both cases, however, advanced signal processing greatly improved contrast of the internal features.

It is well known that the methods of thermographic non-destructive testing based on the thermal contrast are strongly affected by non-uniform heating at the surface. Hence, the results obtained from these methods considerably depend on the chosen reference point. The differential absolute contrast (DAC) method was developed to eliminate the need of determining a reference point that defined the thermal contrast with respect to an ideal sound area. Although, very useful at early times, the DAC accuracy decreases when the heat front approaches the sample rear face. We propose a new DAC version by explicitly introducing the sample thickness using the thermal quadrupoles theory and showing that the new DAC range of validity increases for long times while preserving the validity for short times. This new contrast is used for defect quantification in composite, Plexiglas™ and aluminum samples.

A depth inversion technique by pulsed phase thermography has been recently proposed based on the determination of the blind frequency f b , through phase contrast calculations. The differentiated absolute phase contrast algorithm (DAPhC) is proposed here as an efficient alternative to obtain f b , without the need of a sound area definition. DAPhC is based on the Hough transform to retrieve the phase profiles slopes reducing in the process the human intervention to a minimum. The final result is an image showing the f b values for each pixel. A straightforward relation between the depth and f b can be applied to extract the depth information.

Infrared thermography in nondestructive testing provides images (thermograms) in which zones of interest (defects) appear sometimes as subtle signatures. In this context, raw images are not often appropriate since most will be missed. In some other cases, what is needed is a quantitative analysis such as for defect detection and characterization. In this paper, presentation is made of various methods of data analysis required either at preprocessing and/or processing images. References from literature are provided for briefly discussed known methods while novelties are elaborated in more details within the text which include also experimental results.

Transient thermography (TT) was used for the non-destructive assessment of repaired aircraft carbon or boron composite panels, as well as of composite patches enclosing a fibre optic across their width. The panels were assessed using two different thermographic evaluation systems. In all situations the subsurface features, i.e. defects and/or the fibre optics, were positioned intentionally. After detecting the subsurface features, representative thermal images obtained from the investigation underwent quantitative image analysis. Finally, mathematical thermal modelling was also attempted in order to obtain information about the defects in space and in time.