Results demonstrate the capability of the proposed approach to determine the dimensions, depth, and inclination of the heat source. Further discussion about aspects to pay attention to is provided in the paper. An application of passive thermography is presented in [7]: the detection of defects in photovoltaic plants. Inspecting photovoltaic plants represents nowadays a costly and time-consuming operation. Moreover, the efficiency and profitability of the plants greatly depend on these operations and the considered periodic maintenance procedures. Infrared thermography is used in this work to automate the inspection of these large plants. The proposed method combines infrared thermography and unmanned aerial vehicles, providing a fully automated approach for the (a) detection, (b) classification, and (c) geopositioning of thermal defects in photovoltaic modules. The paper describes the configuration of the mission planner, the acquisition procedure and the proposed image processing pipeline. The automated processing first undistorts the images based on a previous calibration procedure. Then, the correction of the perspective is performed in the images and segmentation is applied to detect the defects. The final step is the conversion from pixel coordinates to GPS coordinates. The proposed system is evaluated using a large dataset of images and standard metrics for classification. Results demonstrate the capability of the system to automate the inspection of large photovoltaic plants, presenting a far more cost-efficient solution than other methods. Infrared thermography very often requires manual analysis and interpretation of the images. This is usually performed using false color mapping, which generates visual representations of the images using a specific color palette. This mapping is problematic, particularly when images contain a wide range of infrared intensities. In [8], a method is proposed to address this issue: adaptive transformation and image fusion. The goal is to transform the raw images into new images with better contrast and clear detail. The proposed approach is divided into several steps: (1) adaptive Gamma transformation is applied to the normalized image, producing multiple brightness levels; (2) each brightness image is decomposed using the multiscale guided filter; (3) enhanced images in each brightness are fused together; (4) the outliers are filtered and images fusion is performed. Testing is performed with images from different public datasets. The experiments compare the performance of the proposed methods with other well-known methods, such as CLAHE. The authors provide a qualitative and quantitative comparison. Results indicate that the proposed method has strength in the detail enhancement of high-dynamic-range infrared images, generating visually pleasing images. In [9], a study is presented comparing the efficiency of time- and frequency-domain analysis techniques in flash thermography, a very popular inspection method using active thermography. The goal is to compare the techniques in terms of defect detectability. The comparison is performed using various carbon-fiber-reinforced polymer samples having a range of defect sizes and defect types. The research work evaluates the advantages and drawbacks of the different postprocessing techniques. In order to quantitatively compare the techniques, the contrast-to-noise ratio is used. The comparison is complemented with the defect-to-background ratio, an alternative defect detectability metric more robust to the noise level of the reference area. The results indicate that the single-bin evaluation approach in the frequency domain provided improved defect detectability than its time-domain counterpart. However, the frequency-domain results also show a higher sensitivity to nonuniform heating. A more in-depth analysis is provided in the paper about other considerations. Another research work on pulsed thermography is presented in [10].