Abstract
A large amount of information needs to be identified and produced during the process of promoting projects of interest. Thermal infrared (TIR) images are extensively used because they can provide information that cannot be extracted from visible images. In particular, TIR oblique images facilitate the acquisition of information of a building’s facade that is challenging to obtain from a nadir image. When a TIR oblique image and the 3D information acquired from conventional visible nadir imagery are combined, a great synergy for identifying surface information can be created. However, it is an onerous task to match common points in the images. In this study, a robust matching method of image pairs combined with different wavelengths and geometries (i.e., visible nadir-looking vs. TIR oblique, and visible oblique vs. TIR nadir-looking) is proposed. Three main processes of phase congruency, histogram matching, and Image Matching by Affine Simulation (IMAS) were adjusted to accommodate the radiometric and geometric differences of matched image pairs. The method was applied to Unmanned Aerial Vehicle (UAV) images of building and non-building areas. The results were compared with frequently used matching techniques, such as scale-invariant feature transform (SIFT), speeded-up robust features (SURF), synthetic aperture radar–SIFT (SAR–SIFT), and Affine SIFT (ASIFT). The method outperforms other matching methods in root mean square error (RMSE) and matching performance (matched and not matched). The proposed method is believed to be a reliable solution for pinpointing surface information through image matching with different geometries obtained via TIR and visible sensors.
Highlights
Thermal infrared (TIR) images are acquired in the approximate range of 9 to 14 μm of the electromagnetic spectrum and applied to various fields, such as 3D building modeling and management [1,2], diagnostics related to fire [3] and heat loss [4], disaster management, a military field that detects abnormalities [7], and the monitoring of safety facilities [8]
In the intensive literature review, it was difficult to find any study that attempted to match images between different geometries and different spectral characteristics, for example, visible nadir-looking vs. TIR oblique and visible oblique vs. TIR nadir-looking imagery
The five matching methods mentioned above were applied for images with different wavelengths and geometries, and the results were compared
Summary
Thermal infrared (TIR) images are acquired in the approximate range of 9 to 14 μm of the electromagnetic spectrum and applied to various fields, such as 3D building modeling and management [1,2], diagnostics related to fire [3] and heat loss [4], disaster management (e.g., an earthquake [5] or volcano [6]), a military field that detects abnormalities [7], and the monitoring of safety facilities (e.g., a nuclear power plant) [8] In this spectral range, it is possible to obtain information even at night, unlike with visible images. During the course of image convergence, image matching between visible and TIR images needs to take place to identify the corresponding points of interest (POIs)
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