In computer vision, optical camera is often used as the eyes of computer. If we replace camera with synthetic aperture radar (SAR), we will then enter a microwave vision of the world. This paper gives a comparison of SAR imaging and camera imaging from the viewpoint of epipolar geometry. The imaging model and epipolar geometry of the two sensors are analyzed in detail. Their difference is illustrated, and their unification is particularly demonstrated. We hope these may benefit researchers in field of computer vision or SAR image processing to construct a computer SAR vision, which is dedicated to compensate and improve human vision by electromagnetically perceiving and understanding the images.
Huynen decomposition prefers the world of basic symmetry and regularity (SR) in which we live. However, this preference restricts its applicability to ideal SR scatterer only. As for the complex non-symmetric (NS) and irregular (IR) scatterers such as forest and building, Huynen decomposition fails to analyze their scattering. The canonical Huynen dichotomy is devised to extend Huynen decomposition to the preferences for IR and NS. From the physical realizability conditions of polarimetric scattering description, two other dichotomies of polarimetric radar target are developed, which prefer scattering IR, and NS, respectively, and provide two competent supplements to Huynen decomposition. The canonical Huynen dichotomy is the combination of the two dichotomies and Huynen decomposition. In virtue of an
Adaptive selection, the canonical Huynen dichotomy is used in target extraction, and the experiments on AIRSAR San Francisco data demonstrate its high efficiency and excellent discrimination of radar targets.
An investigation to the appropriate feature for SAR image registration is conducted. The commonly-used features such
as tie points, Harris corner, the scale invariant feature transform (SIFT), and the speeded up robust feature (SURF) are
comprehensively evaluated in terms of several criteria such as the geometrical invariance of feature, the extraction speed,
the localization accuracy, the geometrical invariance of descriptor, the matching speed, the robustness to decorrelation,
and the flexibility to image speckling. It is shown that SURF outperforms others. It is particularly indicated that SURF
has good flexibility to image speckling because the Fast-Hessian detector of SURF has a potential relation with the
refined Lee filter. It is recommended to perform SURF on the oversampled image with unaltered sampling step so as to
improve the subpixel registration accuracy and speckle immunity. Thus SURF is more appropriate and competent for
general SAR image registration.
This paper is dedicated to investigate the appropriate parameter retrieval algorithm for feature-based synthetic aperture
radar (SAR) image registration. The widely-used random sample consensus (RANSAC) is observed to be instable for its
inappropriate estimation strategy and loss function for SAR images. In order to enable a stable and robust registration for
SAR, an extended fast least trimmed squares (EF-LTS) is proposed which conducts the registration by least squares
fitting at least half of the correspondences to minimize the squared polynomial residuals instead of fitting the minimal
sampling set to maximize the cardinality of the consensus set as RANSAC. Experiment on interferometric SAR image
pair demonstrates that the proposed algorithm behaves very stably and the obtained registration is averagely better than
that by RANSAC in terms of cross-correlation and spectral SNR. By this algorithm, a stable estimation for any kind of
2D polynomial warp model with high robustness and accuracy can be efficiently achieved. Thus EF-LTS is more
appropriate for SAR image registration.