With the development of spaceborne synthetic aperture radar (SAR), ultra-high spatial resolution has become a hot topic in recent years. The system with high spatial resolution requests large range bandwidths and long azimuth integration time. However, due to the long azimuth integration time, many problems arise, which cannot be ignored in the operational ultra-high resolution spotlight mode. This paper investigates two critical issues that need to be noticed for the full-aperture processing of ultra-high resolution spaceborne SAR spotlight data. The first one is the inaccuracy of the traditional hyperbolic range model (HRM) when the system approaches decimeter range resolution. The second one is the azimuth spectral folding phenomenon. The problems mentioned above result in significant degradation of the focusing effect. Thus, to solve these problems, a full-aperture processing scheme is proposed in this paper which combines the superiorities of two generally utilized processing algorithms: the precision of one-step motion compensation (MOCO) algorithm and the efficiency of modified two-step processing approach (TSA). Firstly, one-step MOCO algorithm, a state-of-the-art MOCO algorithm which has been applied in ultra-high resolution airborne SAR systems, can precisely correct for the error caused by spaceborne curved orbit. Secondly, the modified TSA can avoid the phenomenon of azimuth spectrum folding effectively. The key point of the modified TSA is the deramping approach which is carried out via the convolution operation. The reference function, varying with the instantaneous range frequency, is adopted by the convolution operation for obtaining the unfolding spectrum in azimuth direction. After these operations, the traditional wavenumber domain algorithm is available because the error caused by spaceborne curved orbit and the influence of the spectrum folding in azimuth direction have been totally resolved. Based on this processing scheme, the ultra-high resolution spaceborne SAR spotlight data can be well focused. The performance of the full-aperture processing scheme is demonstrated by point targets simulation.

Multiple image group cosegmentation (MGC) aims at segmenting common object from multiple group of images, which is a new cosegmentation research topic. The existing MGC methods formulate MGC as label assignment problem (Markov Random Field framework), which is observed to be sensitive to parameter setting. Meanwhile, it is also observed that large object variations and complicated backgrounds dramatically decrease the existing MGC performance. To this end, we propose a new object proposal based MGC model, with the aim of avoiding tedious parameter setting, and improving MGC performance. Our main idea is to formulate MGC as new region proposal selection task. A new energy function in term of proposal is proposed. Two aspects such as the foreground consistency within each single image group, and the group consistency among image groups are considered. The energy minimization method is designed in EM framework. Two steps such as the loop belief propagation and foreground propagation are iteratively implemented for the minimization. We verify our method on ICoseg dataset. Six existing cosegmentation methods are used for the comparison. The experimental results demonstrate that the proposed method can not only improve MGC performance in terms of larger IOU values, but is also robust to the parameter setting.