Thermal Ir Imaging: Image Quality and Orthophoto Generation

Sledz, A.; Unger, Jakob; Heipke, Christian

doi:10.5194/isprs-archives-xlii-1-413-2018

Cited by 18 publications

(9 citation statements)

References 17 publications

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“…Then, for following 3D model generation, the author used the geometric quality from the processing of RGB images, and the texture was applied from TIR images. A similar idea was presented by [ 3 ]. RGB images were processed by the SfM method and the digital model of surface was generated.…”

Section: Introductionmentioning

confidence: 79%

“…In this paper, an experiment examines if the transformation key is dependent on the scene, and how the scene influences the accuracy of the transformation). In the literature, the images are captured in most of the cases from larger distance, oftentimes from UAV, e.g., [ 2 , 3 , 4 , 6 , 7 , 9 , 11 , 12 , 13 ]. However, interest from the authors of this article involves a close-range application.…”

Section: Introductionmentioning

confidence: 99%

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Transformations in the Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

Dlesk

Vach²,

Pavelka

2021

Sensors

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The photogrammetric processing of thermal infrared (TIR) images deals with several difficulties. TIR images ordinarily have low-resolution and the contrast of the images is very low. These factors strongly complicate the photogrammetric processing, especially when a modern structure from motion method is used. These factors can be avoided by a certain co-processing method of TIR and RGB images. Two of the solutions of co-processing were suggested by the authors and are presented in this article. Each solution requires a different type of transformation–plane transformation and spatial transformation. Both types of transformations are discussed in this paper. On the experiments that were performed, there are presented requirements, advantages, disadvantages, and results of the transformations. Both methods are evaluated mainly in terms of accuracy. The transformations are presented on suggested methods, but they can be easily applied to different kinds of methods of co-processing of TIR and RGB images.

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Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Transformations in the Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

Dlesk

Vach²,

Pavelka

2021

Sensors

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“…The main disadvantage of thermal draping resides in the lack of interrogable quantitative information that could allow operations between multiple (e.g., multitemporal) thermal point clouds, characterized by 3D coordinates (X, Y, Z) and additional temperature attributes. In this framework, co-registered images composed of IR and Visible wavelengths are starting to be used in rigorous engineering approaches [23], while multistage and separate SfM processing appears to be both robust and cost-effective [11,33,43,44].…”

Section: Discussionmentioning

confidence: 99%

3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

et al. 2021

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The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

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“… Photogrammetric processing -the processing was carried out employing rigorous photogrammetric techniques (see Sledz et al, 2018;2020 for details) for the TIR and optical images captured from an UAV. The products of photogrammetric processing are: a thermal orthomosaic containing intensity values (raw sensor data), which are then converted to temperature values; an optical orthomosaic with the same Ground Sampling Distance (GSD) as the thermal one and a DSM, which is derived from optical images, in a raster form with the same GSD as the thermal orthomosaic.…”

Section: Terminology and Overviewmentioning

confidence: 99%

Thermal Anomaly Detection Based on Saliency Analysis From Multimodal Imaging Sources

Sledz

Heipke

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Thermal anomaly detection has an important role in remote sensing. One of the most widely used instruments for this task is a Thermal InfraRed (TIR) camera. In this work, thermal anomaly detection is formulated as a salient region detection, which is motivated by the assumption that a hot region often attracts attention of the human eye in thermal infrared images. Using TIR and optical images together, our working hypothesis is defined in the following manner: a hot region that appears as a salient region only in the TIR image and not in the optical image is a thermal anomaly. This work presents a two-step classification method for thermal anomaly detection based on an information fusion of saliency maps derived from both, TIR and optical images. Information fusion, based on the Dempster-Shafer evidence theory, is used in the first phase to find the location of regions suspected to be thermal anomalies. This classification problem is formulated as a multi-class problem and is carried out in an unsupervised manner on a pixel level. In the following phase, classification is formulated as a binary region-based problem in order to differentiate between normal temperature variations and thermal anomalies, while Random Forest (RF) is chosen as the classifier. In the seconds phase, the classification results from the previous phase are used as features along with temperature information and height details, which are obtained from a Digital Surface Model (DSM). We tested the approach using a dataset, which was collected from a UAV with TIR and optical cameras for monitoring District Heating Systems (DHS). Despite some limitations outlined in the paper, the presented innovative method to identify thermal anomalies has achieved up to 98.7 percent overall accuracy.

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Thermal Ir Imaging: Image Quality and Orthophoto Generation

Cited by 18 publications

References 17 publications

Transformations in the Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

Transformations in the Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

Thermal Anomaly Detection Based on Saliency Analysis From Multimodal Imaging Sources

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