For bistatic inverse synthetic aperture radar (Bi-ISAR) imaging of space targets, the impacts of scatterers’ migration through resolution cell (MTRC) and azimuth high-order phase (AHP) are becoming more severe, especially for a long coherent processing interval (CPI). In this paper, the movement of a scatterer is modeled as a quartic function under the restriction of the space target orbit, then an MTRC-AHP compensation algorithm is proposed. A fourth-order keystone transform is introduced to remove the MTRC firstly, then the residual AHP terms are compensated gradually via the azimuth compensation phase and fractional Fourier transform (FRFT). The impact of the orbit prediction error of the space target on the algorithm is analyzed theoretically. Numerical simulations illustrate the effectiveness of the proposed method and validation of the theoretical analysis.

In this paper, the spatial-variant property of ISAR imaging plane of space targets is exploited for phase unwrapping in bistatic interferometric inverse synthetic aperture radar (InISAR) imaging of space targets. We utilize the interferometric phases in different orbit arcs to obtain the correct fuzzy factor in a calculated set. The threshold for incorrect fuzzy factor excluding and the orbit arc selection strategy are derived for efficiency. Numerical simulations illustrate the effectiveness of the proposed method.

Individual identification of animals is an important means to modernize the livestock industry. In recent years, the research on individual identification of cattle has also received more and more attention. Individual cattle identification is necessary for many important reasons including registration, traceability, production management and animal disease control. Biometric features are unique, which often do not change over time. In this paper, muzzle print is used as biometric feature. The fusion of texture features extracted from Weber Local Descriptor(WLD) and local binary pattern was used to represent individual cattle. Some improvements were made to WLD algorithm. Finally, support vector machine was employed to identify head of cattle from their fusion feature. The proposed method achieved 96.5% identification accuracy.

With the development of image editing software techniques, the content integrity and authenticity of original digital images become more and more important in digital content security. A novel image tampering detection and recovery algorithm based on digital watermarking technology and a chaotic system is proposed, and it can effectively locate the tampering region and achieve the approximate recovery of the original image by using the hidden information. The pseudo-random cyclic chain is realized by the chaotic system to construct the mapping relationship between the image subblocks. It can effectively guarantee the randomness of the positional relationship between the hidden information and the original image block for the better ergodicity of the pseudo-random chain. The recovery value optimization algorithm can represent image information better. In addition to the traditional Level-1 recovery, a weight adaptive algorithm is designed to distinguish the original block from the primary recovery block, allowing $3\times 3$ neighbor block recovery to achieve better results. The experimental results show that the hierarchical tamper detection algorithm makes tamper detection have higher precision. When facing collage attacks and large general tampering, it will have higher recovery image quality and better resistance performance.

Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included.

By exploiting the spatial-variant property of the inverse synthetic aperture radar (ISAR) imaging plane of a space target, interferometric ISAR (InISAR) can provide three-dimensional (3-D) images of space targets using fewer antennas than traditional InISAR imaging systems. This paper analyzes InISAR imaging of space targets with monostatic radar. First, a more realistic ISAR imaging model for space targets is established, which adopts the rotation of the earth and can provide a mathematical apprehension of the movement of space targets in InISAR imaging. Next, a sequential phase unwrapping method for InISAR imaging of space targets with monostatic radar is proposed. Then, the performance indicator of this one-antenna InISAR system is provided with analysis of the impact of the influencing factors in InISAR imaging under different scenarios. Finally, numerical simulations are performed to validate the theoretical analysis and effectiveness of the proposed method.