Abstract
Abstract. Image registration is a fundamental issue in photogrammetry and remote sensing, which targets to find the alignment between different images. Recently, registration of images from difference sensors become the hot topic. The registered images from different sensors are able to offer additional information, which help with different tasks like segmentation, classification, and even emergency analysis. In this paper, we proposed a registration strategy to calculate the dominant orientation difference and then achieve the dense alignment of Thermal Infrared (TIR) image and RGB image with MINDflow. Firstly, the orientation difference of TIR images and RGB images is calculated by finding the dominant image orientations based on phase congruency. Then, the modality independent neighborhood descriptor (MIND) together with global optical flow algorithm are adopted as MINDflow for dense matching. Our method is tested in the image sets containing TIR images and RGB images captured separately but in the same construction site areas. The results show that it is able to achieve the optimal results with features of significance even for dramatically radiometric differences between TIR images and RGB images. By comparing the results with other descriptor, our method is more robust and keep the features of objects in the images.
Highlights
Image registration is a fundamental issue in photogrammetry and remote sensing, which targets to find the alignment between different images
The modality independent neighborhood descriptor (MIND) descriptor and flow matching are adopted as MINDflow for dense matching
Comparing the result with SIFTflow, MINDflow combined with Phasecongruency can better extract the features for dense matching
Summary
Image registration is a fundamental issue in photogrammetry and remote sensing, which targets to find the alignment between different images. Thermal infrared (TIR) images, acquired by thermographic sensors, depict temperature and emission properties of objects. Compared to the near infrared images, thermal infrared images have lower resolutions and different features due to the long wavelength. Since all the objects with a temperature above absolute zeros emit infrared radiation to the environment, thermal infrared images enable us to observe the geometry of objects, their moving process, inside structures, and their thermal properties without sufficient visible illumination (Zin et al, 2007, Weinmann et al, 2014, Christiansen et al, 2014). Considering the issues in the registration of thermal images and RGB images, in this study, we propose a robust method to deal with the dense matching of TIR and RGB images with large radiometric differences.
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