Integrated Matching and Geocoding of SAR and Optical Satellite Images

Geometrical Matching of SAR and Optical Images Utilizing ASIFT Features for SAR-based Navigation Aided Systems

“…The process of image registration refers to the alignment of two or more images of the same scene which might be obtained with the same sensor, time, and imaging conditions, as well as by different sensors and viewpoints. The process of orientating optical and SAR images is still an open issue, which creates many challenges [9][10][11][12][13][14][15][16][17][18][19]. Many issues can be dealt with in the process of the synergy of optical and SAR data, due to the great differences between passive and active remote sensing techniques.…”

“…Another issue is related to the differences in the image geometry acquired from these two devices [9,[11][12][13]15,16,18,19]. Thus, many co-registration approaches have been proposed (e.g., [9][10][11][12][13][14][15][16][17][18][19]), but, in general, these approaches might be divided into two main categories: area-and feature-based methods. Nowadays, the Structure from Motion (SfM) methods, which are mostly based on the feature-based approach, are used for the co-registration of spaceborne SAR and optical images [9,[11][12][13]15,16,18,19].…”

“…The presented technique was intensively tested, validated, and compared with the methods existing in the literature. Especially two approaches were investigated [9,17]. However, the method described in [17] is inefficient and fails due to the speckle noise on SAR images.…”

“…Especially two approaches were investigated [9,17]. However, the method described in [17] is inefficient and fails due to the speckle noise on SAR images. The approach presented in [9], in turn, is accurate only on urban areas, and such assumption cannot be applied in the considered case.…”

Comparative evaluation of signal-based and descriptor-based similarity measures for SAR-optical image matching

“…For example, Lehereau et al [5] estimate the translations between a SAR and an optical image by exploiting the Fourier-Mellin invariant calculated from line and edge images, respectively. Hellwich et al [6] integrate matching with geocoding in order to robustify the results of classical tie point matching based on SIFT and SURF features. Incorporating prior knowledge in the form of previously extracted roundabouts and junctions, Palubinskas & Reinartz [7] employ template-based matching for identification of sparsely distributed, yet robust tie points.…”