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An iteratively reweighted complex approximate message passing algorithm is proposed for cluster sparse inverse synthetic aperture radar imaging. The proposed algorithm improves the recovery performance of cluster sparse signal. A pattern-coupled weight update rule is introduced to establish a coupling relation between the sparsity patterns of neighboring coefficients. Real radar data results demonstrate...
In this letter, an attitude estimation method is presented for space targets by using an inverse synthetic aperture radar (ISAR) image sequence. The line structures, like the boundaries of planar payloads, are extracted from the ISAR image sequence and associated from frame to frame. With the accommodation of the radar looking angle information from the trajectory, the threedimensional attitude of...
In this paper, we proposed an image-based rendering method based on the block sparsity of epipolar plane image(EPI). This method considers the structural similarity and block sparseness features of scene signal described by EPI model, which makes it possible to estimate approximately original signal with less measured values, and reduces the complexity of signal sampling and processing. First, we...
In this paper, a novel autofocus imaging method is proposed to achieve high-resolution for inverse synthetic aperture radar (ISAR) in the compressive sensing (CS) framework. Firstly, we fomulate the ISAR CS imaging in a Multiple Measurement Vector (MMV) sparse optimization problem. Then, by utilizing the structure sparsity of ISAR image, i.e. row sparsity and column sparsity simutaneously, our method...
Patch matching is the key step in patch-based methods, including local matching and global matching. The global matching achieves high accuracy, but the running time is relatively long; on the contrary, the local matching reduces the running time, but also decreases the accuracy. To reduce the running time and preserve the accuracy, a new local-global mixed patch matching framework is proposed. Within...
This paper proposes a classification method based on principal component reconstruction (PCR) for target recognition in synthetic aperture radar (SAR) image. To characterize the SAR image and alleviate the influence of different intensity of the same targets on target recognition, the SAR image is mapped into the principal component space by the principal component analysis with zero mean. In the...
Combining traditional inverse synthetic aperture radar (ISAR) imaging, interferometry technique and compressed sensing (CS), we studied the problem of interferometric ISAR imaging with sparse aperture. Based on the elaborate analysis of smoothed l0 algorithm in CS, we proposed joint smoothed l0 algorithm which is capable of maintaining coherence between ISAR images of different radars under sparse...
The deep learning neural network is a recent development that has become the subject of research in the computer vision and remote sensing disciplines. Super resolution (SR) images can be obtained using deep neural network methods that achieve a higher performance than all previous traditional methods. Here, in this study, the objective is to describe existing deep learning methods for SR satellite...
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