Abstract
Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.
Highlights
IntroductionRemote sensing image registration refers to the fundamental task in image processing to align two or more images of the same scene (i.e., the sensed images and the reference images), which can be multiview (obtained from different viewpoint), multitemporal (taken at different times) and multisource (derived from different sensors)
Remote sensing image registration refers to the fundamental task in image processing to align two or more images of the same scene, which can be multiview, multitemporal and multisource
Since no inlier set is outputted from GLMDTPS, the compared methods are SIFT, coherent point drift (CPD) and ours. (II) Quantitative comparison and Qualitative demonstration on image registration are carried out on all the methods using the root of mean square error (RMSE), mean absolute error (MAE) and standard deviation (SD). (III) By using the different datasets, quantitative and qualitative demonstration on feature matching and image registration are carried out to examine the availability and robustness of our method using the precision ratio (PR), RMSE, MAE and SD
Summary
Remote sensing image registration refers to the fundamental task in image processing to align two or more images of the same scene (i.e., the sensed images and the reference images), which can be multiview (obtained from different viewpoint), multitemporal (taken at different times) and multisource (derived from different sensors). We mainly focus on registering the remote sensing images taken from different viewpoint. Existing remote sensing image registration methods can be approximately classified into two categories: area-based methods and feature-based methods. The captured remote sensing images exist the local non-rigid geometric distortions caused by ground relief variations and imaging viewpoint changes, we mainly focus on feature-based methods for registration. Such methods generally consists of three steps [7]: (i) feature descriptors extraction; (ii) feature point sets registration; (iii) image transformation and resampling
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