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# PCA-enhanced Computational Thermography for the Non-destructive Investigation of the Historic Bücker Bü 181 Aircraft

Sruthi Krishna KUNJI PURAYIL ¹, Mathias RÖLLIG ¹, Philipp Daniel HIRSCH ¹, Somsubhro CHAUDHURI ¹, Julien LECOMPAGNON ¹, Lutz STROBACH ², and Mathias ZIEGLER ¹

¹ Division 8.3: Thermographic Methods, Federal Institute for Materials Research and Testing (BAM), Berlin, Germany; sruthi-krishna.kp@bam.de, mathias.roellig@bam.de, philipp-daniel.hirsch@bam.de, somsubhro.chaudhuri@bam.de, julien.lecompagnon@bam.de, mathias.ziegler@bam.de

² Berlin University of Technology and Economics (HTW), Berlin, Germany; lutz.strobach@htw-berlin.de

## Abstract

Infrared thermography is a widely recognized non-destructive testing (NDT) method used in material research and defect detection across various industrial applications. Moreover, thermography plays a crucial role in preserving cultural heritage, including historical paintings and buildings. This study focuses on the application of thermography in inspecting the historic Bücker Bü 181 aircraft, which was used in Germany during World War II. Over time, the original appearance of aircraft has often been altered as part of preservation efforts, either before or during their time in museums, leading to deviations from their historically original state. Additionally, the operational history of such objects is frequently undocumented or entirely lost, making it difficult to understand the presence of artifacts and historically significant data. These factors present major challenges in cultural heritage preservation, and destructive methods cannot be used to investigate such invaluable objects. Therefore, thermography is implemented as a non-destructive and contactless examination method. Active flash thermography combined with phase analysis is a powerful tool for evaluating multilayer systems. In this study, multiple layers of old paint on the object posed a challenge in assessing defect conditions and retrieving other critical information beneath the surface coatings. Nevertheless, pulse thermography not only demonstrated its capability to identify defects and markings in multilayered coatings but also provided insights into the internal structure and subsections of the investigated aircraft.

**Keywords:** Non-destructive testing, infrared thermography, cultural heritage, defect detection, multilayer coatings

## 1. Introduction

The preservation of historical objects, particularly aircraft, poses major challenges due to material deterioration and modifications made over time [1]. Many historic aircraft, such as the Bücker Bü 181, an aircraft that was used in Germany during World War II, have undergone multiple (re)paint jobs, restorations, and structural strengthening. These preservation methods may mask original markings and inscriptions that provide valuable information about their operational history, ownership, and maintenance records [2]. Recovering these hidden details is critical for historical documentation and preservation efforts. Traditional investigative methods, such as mechanical removal of paint layers, are invasive and may damage the object. In contrast, the application of nondestructive testing (NDT) techniques enables the study of subsurface features while preserving the artifact's integrity [3]. This has led to the application of NDT gaining much popularity in the field of heritage science and aerospace conservation, where preserving material integrity is of great importance [4]. Compared to conventional chemical and mechanical inspection methods, which risk damaging fragile historical artifacts, NDT provides a contactless testing and evaluation mode. Among the various NDT methods, X-ray imaging and infrared thermal imaging are commonly used to test objects to study the surface and subsurface defects and uncover hidden features in heritage objects [5], [6].

Infrared thermography (IRT) has been widely used in historic building conservation, aircraft maintenance, and art analysis. Among various IRT techniques, pulsed infrared thermography is one possible technique that is mainly used in detecting subsurface defects due to its ability
to identify the thermal contrast associated with material inhomogeneities. It is therefore
capable of detecting hidden layers in paintings, and hidden markings, and allows to
identify delaminations in aging aircraft components [7]. Pulsed thermography captures an
object's thermal response after a brief heat excitation, allowing the assessment of surface
and near-subsurface features based on the thermophysical quantities (thermal conductivity,
density of mass and heat capacity) and emissivity. However, raw thermal images often show
low contrast, noise, and complex temperature gradients, making direct interpretation often
difficult. To improve visibility and extract useful information, advanced image processing and
computational techniques are necessary.

This study discusses a computationally enhanced thermographic approach to expose hidden markings on the Bücker Bü 181 aircraft by combining principal component analysis (PCA), wavelet-based feature extraction, contrast enhancement, and clustering-based segmentation. PCA is used to transform the high-dimensional thermal image dataset (time sequence of high-resolution thermograms) into a low-dimensional set of orthogonal components, removing noise while emphasizing significant thermal variations related to surface and subsurface features [8]. The top principal components are then fused together to produce an improved visualization of the thermal data. To improve text visibility, contrast-limited adaptive histogram equalization (CLAHE) and the continuous wavelet transform (CWT) are used to improve localized intensity variations [9], [10]. In addition, K-means clustering is also used to separate writings from their backgrounds for the classification of thermographic patterns [11].

The primary objective of this study is to investigate the effectiveness of PCA-enhanced thermographic processing for non-destructive detection of hidden markings on historical aircraft. This study compares the feasibility of feature extraction and segmentation strategies in extracting obscured hidden markings using two approaches: PCA with wavelet-based enhancement and PCA with clustering-based segmentation. The results aim to contribute to non-invasive cultural heritage preservation by providing insights on how thermographic imaging can be optimized for historical documentation, aircraft restoration, and forensic investigations.

## 2. Methodology

### 2.1 Pulsed Infrared Thermography

Pulsed infrared thermography is a method from active IRT used to analyze the thermal response of materials when subjected to a brief heat pulse. This technique involves applying a thermal excitation of short duration to the surface of the Bücker Bü 181 aircraft, after which an infrared camera captures the resulting heat dissipation over time. The differences in the thermophysical quantities and emissivity across various regions of the aircraft help reveal hidden structural features, defects, and potential inscriptions beneath the paint layers. Figure 1. shows the experiment setup, which consists of two flash lamps, each with 6 kJ input energy to provide uniform thermal pulse excitation on the surface of the aircraft. A mid-wavelength IR (MWIR) camera with a wavelength range of 3 µm to 5 µm is used in reflection configuration to capture thermal images with a acquisition frequency of 50 Hz and an image size of 640 × 512 pixels. Figure 2. (a) and (c) shows visual images, and (b) and (d) show raw thermal images from the left wing and fuselage of the Bücker Bü 181 aircraft, respectively. These thermal images shown are from a single timestamp (i.e., at a specific point in time within the thermography sequence)
in which writings under the paint layers are partially visible. These thermal responses were
then analyzed using advanced computational techniques to extract meaningful patterns while
minimizing noise and irrelevant thermal variations.

<figure><img src="image_2.png" /><figcaption>Figure 1. Pulsed infrared thermography experiment setup consisting of two flash lamps as a heat source and an MWIR camera for capturing the thermal response from the aircraft's left wing surface.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 2. (a) and (c) show visual images, and (b) and (d) show raw thermal images from the left wing and fuselage of the Bücker Bü 181 aircraft, respectively.</figcaption></figure>
## 2.2 Data analysis

The acquired thermal data was analyzed using a combination of machine learning and image processing techniques to uncover hidden texts and structural anomalies. The data analysis consisted of two approaches: PCA-based feature extraction with contrast and wavelet enhancement and PCA-based clustering for segmentation. These methods were designed to maximize the visibility of subsurface details while ensuring accurate text detection.

### 2.2.1 PCA with CLAHE and CWT

To improve the visibility of hidden texts, Principal Component Analysis (PCA) was applied to the thermal image sequence. PCA is a dimensionality reduction technique that transforms high-dimensional thermal data into a set of orthogonal components, capturing the most significant variations. Figure 3., shows the illustration of the first image enhancement method, which consists of PCA with CLAHE and CWT. The overall image enhancement consists of PCA, contrast enhancement of the PCA dominant components fused image, and feature enhancement by CWT and adaptive thresholding to extract the hidden text. By decomposing the thermal images into principal components, the dominant thermal features were preserved while random noise and redundant information were filtered out. Instead of using the entire dataset, only the top 5–10 principal components were selected and fused, as these captured key thermal components associated with material discontinuities and text beneath the paint layers.

<figure><img src="image_4.png" /><figcaption>Figure 3. Illustration of the image enhancement method 1: PCA with CLAHE and CWT.</figcaption></figure>

Following PCA transformation, contrast enhancement techniques were employed to further improve feature visibility. Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied to improve the local contrast, ensuring that faint markings were visible against the background. This step helped to improve the differentiation of text-like structures from the surrounding material textures. To refine the extracted features further, continuous wavelet transform (CWT) was implemented to enhance fine details and text edges. CWT was particularly useful in identifying localized variations in intensity, allowing for better edge definition of characters and potential markings.

### 2.2.2 PCA with K-means Clustering

While PCA and wavelet-based enhancement improved feature visibility, clustering techniques are used to separate text-like structures from the background. To achieve this, K-means clustering was applied to the PCA-enhanced thermal images, grouping pixels with similar thermal characteristics into distinct clusters. Figure 4. shows the illustration of the second image enhancement method, PCA with K-means clustering. The first step involved extracting PCA components from the thermal dataset, similar to the previous method. However, instead of applying edge enhancement techniques, a fused image with the first few principal components (PC1—PC5) was used as input features for clustering. K-means clustering, an unsupervised machine learning algorithm, was then applied to categorize the image into distinct clusters, corresponding to different thermal regions on the aircraft surface. This segmentation approach
enabled the automatic isolation of text regions and background material.

<figure><img src="image_5.png" /><figcaption>Figure 4. Illustration of the image enhancement method 2: PCA with K-means Clustering</figcaption></figure>

**3. Results and discussion**

This section presents a comparative analysis of two different methodologies applied to the thermal imaging data to enhance hidden texts and markings on the Bücker Bü 181 aircraft in the areas highlighted in Figure 2.. The effectiveness of PCA-based feature extraction, contrast enhancement, clustering, and wavelet-based techniques is discussed in detail. The first method involved fusing PCA components, followed by contrast enhancement and wavelet-based edge detection to extract hidden markings. The second method applied PCA-based feature fusion, followed by K-means clustering for character segmentation. Both methods were aimed at improving the visibility of faint surface-level texts beneath the aircraft's layered coatings.

In both approaches, PCA was utilized to extract key features from the thermal images while reducing noise. The first principal component (PC1) primarily captured the dominant thermal variations across the aircraft's surface, influenced by differences in material composition. However, higher-order components, particularly PC2 to PC5, revealed localized anomalies that were not visible in the original thermal images. These anomalies corresponded to potential surface markings or subsurface features of historical significance. The fused PCA image, as shown in Figure 5 a (i) and Figure 5 b (i), helped in highlighting the texts hidden under paint layers, especially in Figure 5 b (i). Although PCA effectively reduced noise and highlighted fine details, an excessive number of components led to the retention of some redundant information, making it necessary to carefully select the most relevant features. The visibility of hidden markings improved significantly after PCA fusion and contrast enhancement, which facilitated the subsequent segmentation process. However, in some regions, faint markings remained obscured, suggesting that additional processing steps, such as adaptive filtering or segmentation, could further improve feature extraction.

**3.1 Method 1: PCA with CLAHE and CWT**

In the first method, the fused PCA-enhanced image underwent contrast enhancement, followed by feature enhancement techniques, to reveal hidden text and structural features. The use of CLAHE greatly improved the visibility of the text compared to the background noise, making the thermal differences more prominent. Next, a CWT was applied to enhance fine details by improving edges and localized features. Figure 5 shows the result images at each stage of the enhancement process on the raw thermal image sequence from the left wing (Figure 5 a) and the fuselage (Figure 5 b). In Figure 5 a-b (i), the fused PCA component, with hidden features that are not clearly visible. In Figure 5a-b(ii), the resulting enhanced image using contrast and wavelet feature enhancements is shown. Here, the hidden texts are more visible compared to the fused PCA image.
In Figure 5 a-b (iii), the results after adaptive thresholding on the feature-enhanced image for the text segmentation are shown. Figure 5 b (iii) shows better text segmentation compared to Figure 5 a (iii) because of the thick top paint layer on the fuselage (as compared to the left wing) restricting thermal diffusion through the specimen. As shown in Figure 5., the wavelet-based feature extraction proved effective in distinguishing characters from the surrounding material textures.

The results of this method demonstrated that wavelet-based enhancement was particularly useful in refining the fine structural details of the inscriptions, allowing for better character localization. However, over-enhancement in certain areas introduced artificial edge artifacts, which made distinguishing between actual text and noise more challenging. Some deeper markings remained faint despite the enhancement process, indicating that multi-frequency or phase-based thermography could be explored to penetrate deeper layers. Additionally, certain regions exhibited high contrast variations that resulted in the loss of subtle features, emphasizing the need for adaptive processing techniques.

<figure><img src="image_6.png" /><figcaption>Figure 5. Method 1: (a) Left wing: (i) Fused PCA component, (ii) Contrast and feature enhanced image using CLAHE and CWT, and (iii) result after adaptive thresholding of the raw thermal image Figure 2 b; (b) Fuselage: (i) Fused PCA component, (ii) Contrast and feature enhanced image using CLAHE and CWT, and (iii) result after adaptive thresholding of the raw thermal image Figure 2 d</figcaption></figure>

## 3.2 Method 2: PCA with K-means Clustering

The second method combines PCA-enhanced features with K-means clustering to segment text-like structures from the background. By grouping similar pixel intensities into distinct regions, this approach successfully isolated hidden markings embedded in the aircraft's coating layers. Unlike Method 1, which focused on feature extraction through edge enhancement, Method 2 emphasized structural segmentation, providing a clearer distinction between the characters and surrounding material textures.
The results, as shown in Figure 6 a-b (i), show that the PCA-fused image provided a strong foundation for clustering, enabling the detection of distinct text regions. In Figure 6 a-b (ii), the result of K-means clustering with the number of clusters is shown: $k = 4$ for the left wing and $k = 6$ for the fuselage. The result of K-means clustering distinctly highlighted regions with different thermal properties, correlating with potential inscriptions. However, while this approach provided better segmentation compared to raw thermal image clustering, some character boundaries remained unclear due to minor intensity variations. Additionally, certain regions of the aircraft's surface were misclassified due to overlapping thermal intensities, requiring manual fine-tuning of clustering parameters. Despite these limitations, the integration of PCA with K-means clustering demonstrated an effective approach for isolating inscriptions and could be further refined with post-processing techniques such as morphological filtering or deep learning-based segmentation.

<figure><img src="image_7.png" /><figcaption>Figure 6. Method 2: (a) Left wing: (i) Fused PCA component, (ii) Result of K-means clustering ($k = 4$); (b) Fuselage: (i) Fused PCA component, (ii) Result of K-means clustering ($k = 6$)</figcaption></figure>

### 3.3 Comparison of the two methods

The comparative analysis of both methods revealed distinct advantages and limitations. Method 1 (PCA with CLAHE and CWT) effectively enhanced text visibility, making it particularly useful for feature extraction, but it also introduced artifacts that required careful parameter tuning. In contrast, Method 2 (PCA with K-means Clustering) provided a more structured segmentation of text regions but resulted in overlapping of different clusters, which made it only possible to extract part of the texts. The clustering approach required additional refinement to improve character boundary definitions. Overall, Method 1 was found to be more suitable for highlighting fine structures and edge details, while text was almost visible in the raw thermal image. A hybrid approach that uses wavelet-based enhancement along with clustering-based segmentation could provide a better way to detect characters and extract features.
## 4. Conclusion

This paper demonstrated the preliminary study towards the application of pulsed infrared thermography and a feasibility check of advanced computational techniques for non-destructive evaluation of the Bücker Bü 181 aircraft. The primary goal was to uncover hidden writings beneath layers of paint and coatings. This approach, which included PCA, contrast enhancement, wavelet-based feature extraction, and K-means clustering, enabled the effective identification and segmentation of historical markings that were otherwise obscure in raw thermal images. The data indicates that PCA was helpful in improving relevant thermal features while lowering background noise. Relevant thermal signals corresponding to material discontinuities and inscriptions were maintained and increased by focusing on the most relevant major components. The contrast enhancement technique CLAHE enhanced the visibility of faded text in the PCA component fused image by changing local intensity distributions. Furthermore, the CWT improved sharp intensity transitions while also enhancing edge features and localized patterns in thermal images. While these strategies increased the visibility of hidden writings, segmentation remained difficult. To solve this, PCA-based clustering with K-means was also implemented on the PCA-fused image to divide pixels into separate thermal areas, separating possible text from background noise. The qualitative results showed that clustering using PCA-reduced features gave better text segmentation than direct intensity-based clustering because it grouped comparable thermal qualities together. However, some misclassifications were identified, particularly in locations with overlapping thermal gradients, emphasizing the need for additional clustering parameter adjustment. The comparison of two independent approaches — PCA with contrast and wavelet-based enhancement and PCA with clustering-based segmentation — showed each method's strengths and limitations. In this study, hidden writings located beneath multiple layers of paint were visible even in the raw thermal images at different timestamps, demonstrating the promising potential of infrared thermography requiring minimal post-processing as a tool for cultural heritage preservation.

Beyond the scope of this preliminary research, the findings have important implications for historical aircraft conservation preservation. The ability to uncover hidden markings non-destructively provides significant information about the operating history and maintenance records of older aircraft. Furthermore, the technology shown here can be applied to other heritage objects, such as old manuscripts, buildings, paintings, and archaeological artifacts, providing a non-destructive and non-invasive approach to cultural heritage preservation. Despite the success of this method, a number of obstacles persist. The depth constraints of pulsed thermography prevent the detection of deeper subsurface structures. Future research should investigate multi-frequency or phase-based thermography approaches to improve subsurface detection capabilities [12], [13]. Furthermore, machine learning and deep learning models could be incorporated into the workflow to automate text classification and segmentation, increasing detection accuracy. Supervised learning methods, such as convolutional neural networks (CNNs) trained on labeled thermal datasets, may dramatically enhance character identification and fault classification. Finally, our study proved that PCA-enhanced thermographic analysis is a useful method for non-destructive feature extraction and segmentation of concealed inscriptions on vintage aircraft. By improving and integrating modern computational methodologies, the suggested methodology provides a scientifically sound approach to aerospace conservation, cultural heritage preservation, and non-destructive testing. Future studies will focus on improving segmentation accuracy, investigating deeper
heat penetration techniques, and incorporating AI-based automation to improve character
identification.

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