An Image Registration Method for Multisource High-Resolution Remote Sensing Images for Earthquake Disaster Assessment  
[PERSON]  
1 College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China; [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (P.W.) [PERSON]  
22  
played an important role in the rapid acquisition and dynamic monitoring of earthquake disaster information and post-earthquake reconstruction. The data processing for extracting earthquake disaster information from RS images includes image preprocessing, disaster information extraction, and disaster assessment [4]. The accuracy and efficiency of image preprocessing, mainly referring to geometric rectification of RS images and the coregistration of multisource RS images, are crucial for the accuracy and efficiency of disaster information extraction. In order to obtain disaster information quickly and support disaster responses, it is particularly important to have geometrically accurate RS images available shortly after the acquirement of post-earthquake RS images. Using the images to extract the disaster information, including the location, distribution, and extension, is very crucial for disaster relief and rescue. For instance, in transportation planning processes after an earthquake, a fast and accurate assessment of the destruction degree of traffic facilities (such as roads and bridges) is essential to have an updated configuration of infrastructures to facilitate disaster relief work [5]. The accuracy of coregistration of the post-earthquake and pre-earthquake RS images is particularly important when change detection methods are used for disaster information extraction. With the development of RS technology, different satellite sensors can provide multispectral, multitemporal, and multiplatform RS images. Additionally, the spectral resolution of RS images is also continually improving, and image registration research has gradually shifted from medium-low resolution to high resolution [6]. The registration of high-resolution RS images is significantly more challenging than that of medium-low-resolution RS images, since high-resolution RS images contain more intricate details and texture information [7]. Therefore, the coregistration of multisource high-resolution RS images has been a research focus in RS image processing. Image registration is the process of the geometric registration of two or more images covering the same area and acquired at different times, from different viewpoints, or by different sensors [8]. The majority of the automatic registration methods for RS images can be classified into two categories, including gray-based (or intensity-based) methods and feature-based methods [9; 10]. To extract tie points from the two images, the gray-based methods establish a similarity measure between the two images through the mutual information (MI) method [11; 12] or normalized cross-correlation method (NCC) [13]. As gray values of pixels within the neighborhood of each pixel need to be considered, this kind of method suffers from a large number of calculations and relatively low efficiency. The feature-based methods locally extract points, lines, and regional features for image registration [14]. Due to the advantages of easy acquisition, short running time, and high robustness, point features are widely used in image registration. Currently, many feature-based methods have been successfully used for RS image registration, such as the [PERSON] corner detection algorithm [15], the [PERSON] corner detection algorithm [16], the Shi_[PERSON] corner detection algorithm [17], the scale-invariant feature transform (SIFT) [18], speeded up robust features (SURF) [19], and the features from accelerated segment test (FAST) [20]. The performances of several different algorithms, including SIFT, shape context [21], steerable filters [22], principal components analysis-SIFT (PCA-SIFT) [23], differential invariants [24], and other methods, were compared by [PERSON] and [PERSON] [25].