# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
###### Abstract
Therefore, the effectiveness of area-based registration methods is limited when it comes to registering thermal images with visible-light images. Compared with the region-based method, the feature-based method does not directly process the image intensity value, but uses more advanced feature information, such as points, lines, edge contours and regional features. After the point feature is detected and matched, its coordinate position can be directly utilized for the computation of the transformation model parameters. This kind of detection and registration method using feature points is almost not affected by the change in the overall pixel strength of the image, which is conducive to adapting to the change in illumination or dealing with the analysis of multiple acquisition devices. Scholars' research on feature-based methods is presented in the following paragraphs. [PERSON] proposed a scale-invariant feature transform (SIFT) [24] for feature point matching. Firstly, the feature points in the scale space are found by calculating the extreme points in the Gaussian difference pyramid [25]. Then, the key point descriptor is generated by histogram statistics. Finally, the matching is completed by the feature vector of the key points. Because of its good scale and rotation invariance, SIFT has become one of the most effective local feature description algorithms, and many scholars have also proposed improved algorithms based on SIFT [26; 27; 28; 29; 30]. [PERSON] et al. [31] proposed an improved algorithm, Speeded Up Robust Features (SURF), based on SIFT. Firstly, the image is transformed by the Hessian matrix, and the matrix determinant is calculated to detect the extreme point. Secondly, the box blur is used to find the Gaussian blur approximation. Finally, the feature points are described by haar wavelet transform. Compared with SIFT, this method significantly enhances operational efficiency without compromising the accuracy of the outcomes. [PERSON] et al. [32] proposed the feature-matching algorithm Oriented FAST and Rotated BRIEF (ORB) for real-time alignment, which first performs key point extraction using improved FAST, and then conducts a feature description using BRIEF, which is two orders of magnitude faster than SIFT. For the aerial image fusion alignment problem, [PERSON] et al. [33] used a particle swarm optimization (PSO)-based algorithm as a search strategy, and an alignment metric (AM) for judgment, to achieve multi-source image alignment for infrared and visible light. [PERSON] et al. [34] presented the Partial Intensity Invariant Feature Descriptor (PIIFD) algorithm, a multimodal image alignment approach. The Harris detector [35] is used to detect corner points and determine the principal direction for each corner point. PIIFD is then applied to describe the features, effectively addressing the issue of alignment for low-quality retinal images. [PERSON] et al. [36] proposed a scale-invariant multi-scale descriptor SI-PIIFD for feature description (scale-invariant PIIFD). This method uses a set of variable size windows (40 \(\times\) 40,36 \(\times\) 36, 32 \(\times\) 32, 28 \(\times\) 28, 24 \(\times\) 24, and 20 \(\times\) 20) for feature description. The best scale information is obtained through the majority voting strategy, and the window size of the image of the same information source is fixed to improve the matching accuracy. To a certain extent, scale invariance is realized, but this only considers the scale differences caused by image resolution, and the image blur caused by different sensors will also seriously affect the feature information of the image. Therefore, SI-PIIFD still cannot achieve continuous scale invariance. [PERSON] et al. [37] used a Harris detector to detect feature points in Gaussian blurred images at different scales, which can achieve a better scale invariance. With the continuous advancements in machine learning in the field of computer vision at the present stage, methods based on deep learning [38; 39; 40] have emerged.