The improved phase congruency (PC) algorithms have been successfully applied to optical and synthetic aperture radar (SAR) image registration since they are insensitive to nonlinear radiometric and geometric differences. However, most of the algorithms are sensitive to large-scale differences and rotation differences between optical and SAR images. To tackle this, we propose a PC framework to register optical and SAR images. It is compatible with large-scale and rotation invariance. Firstly, a multi-scale Harris keypoint extraction method based on the maximum moment of PC (named PC-Harris) is proposed. The scale space is constructed by combining PC with the log-Gabor filter. Secondly, we propose a PC model to construct the feature descriptors. The orientation and amplitude responses are obtained based on the PC model. Meanwhile, the novel descriptor is constructed based on the polar coordinate system and thus can handle the scale and rotation differences between optical and SAR images. Finally, outliers are removed by the fast sample consensus (FSC). The experiments conducted on several optical and SAR images verify the effectiveness of the proposed framework.

Fast steering mirror (FSM) is an efficient and reliable mechanical device in aerial optical image systems for controlling the beam direction with high precision. With the advantages of compact size, high speed, simple structure, and long linear stroke, voice coil motors are ideal actuators for FSM systems. However, model uncertainty can lead to poor performance or even system divergence, especially in environments with temperature variations, electromagnetic environment changes, etc. This paper proposes a novel finite-time adaptive control (FAC) algorithm for an FSM system to obtain high performance, i.e., positioning accuracy, dynamic performance, and robustness. In addition, the finite-time convergence of the controller is analyzed. In the experiments, the controller is implemented in a DSP-based microprocessor. The step response results show that the proposed algorithm has a shorter setting time, smaller overshoot, and smaller steady-state error compared to classical sliding mode control (SMC). The sinusoidal signal tracking accuracy of FAC + SMC has been improved by 19.8%. In addition, as the model uncertainty increases 10%, the root mean square errors (RMSEs) are 1.73″ and 1.18″ for SMC and FAC + SMC, respectively. With 20% model uncertainty, the RMSEs increase to 2.56″ and 1.85″, respectively. Extensive experiments demonstrate the general effectiveness of the proposed algorithm.

In order to improve the geo-location accuracy of the airborne optoelectronic platform and eliminate the influence of assembly systematic error on the accuracy, a systematic geo-location error correction method is proposed. First, based on the kinematic characteristics of the airborne optoelectronic platform, the geo-location model was established. Then, the error items that affect the geo-location accuracy were analyzed. The installation error between the platform and the POS was considered, and the installation error of platform’s pitch and azimuth was introduced. After ignoring higher-order infinitesimals, the least square form of systematic error is obtained. Therefore, the systematic error can be obtained through a series of measurements. Both Monte Carlo simulation analysis and in-flight experiment results show that this method can effectively obtain the systematic error. Through correction, the root-mean-square value of the geo-location error have reduced from 45.65 m to 12.62 m, and the mean error from 16.60 m to 1.24 m. This method can be widely used in systematic error correction of relevant photoelectric equipment.

In the field of airborne aerial imaging, image stitching is often used to expand the field of view. Registration is the foundation of aerial image stitching and directly affects its success and quality. This article develops a fast binary image registration method based on the characteristics of airborne aerial imaging. This method first integrates aircraft parameters and calculates the ground range of the image for coarse registration. Then, based on the characteristics of FAST (Features from Accelerated Segment Test), a new sampling method, named Weighted Angular Diffusion Radial Sampling (WADRS), and matching method are designed. The method proposed in this article can achieve fast registration while ensuring registration accuracy, with a running speed that is approximately four times faster than SURF (Speed Up Robust Features). Additionally, there is no need to manually select any control points before registration. The results indicate that the proposed method can effectively complete remote sensing image registration from different perspectives.

The local optimality of motion vectors (MVs) is an intrinsic property in video coding, and any modifications to the MVs will inevitably destroy this optimality, making it a sensitive indicator of steganography in the MV domain. Thus the local optimality is commonly used to design steganalytic features, and the estimation for local optimality has become a top priority in video steganalysis. However, the local optimality in existing works is often estimated inaccurately or using an unreasonable assumption, limiting its capability in steganalysis. In this paper, we propose to estimate the local optimality in a more reasonable and comprehensive fashion, and generalize the concept of local optimality in two aspects. First, the local optimality measured in a rate-distortion sense is jointly determined by MV and predicted motion vector (PMV), and the variability of PMV will affect the estimation for local optimality. Hence we generalize the local optimality from a static estimation to a dynamic one. Second, the PMV is a special case of MV, and can also reflect the embedding traces in MVs. So we generalize the local optimality from the MV domain to the PMV domain. Based on the two generalizations of local optimality, we construct new types of steganalytic features and also propose feature symmetrization rules to reduce feature dimension. Extensive experiments performed on three databases demonstrate the effectiveness of the proposed features, which achieve state-of-the-art in both accuracy and robustness in various conditions, including cover source mismatch, video prediction methods, video codecs, and video resolutions.