Imaging by the remote sensing camera with a wide field of view (WFV) is of great significance in the issue of polar environment. However, the instability of the image motion velocity poses a huge challenge to the observation task since the direction of the earth's rotation velocity keeps changing in the polar regions. The edge blur of polar image by the camera with the WFV is usually ignored. Therefore, a specific theoretical model that the camera images over the polar region is supposed to describe the instability distinctly. Mathematical expressions of the image motion velocity field are obtained with a novel method of velocity projection and coordinate transformation. The quantitative analysis of simulation reveals that the increasing anisotropy of the instantaneous image motion velocity field gets most significant near the poles. The value of modulation transfer function at the edge of the field of view decreased by 0.33%, which results in a discrepancy of sharpness in an image. The model is capable of explaining the edge blur, of the image from BNU-1, which provides a theoretical basis for the image motion compensation strategy of cameras with the WFV.

Infrared small target detection is a challenging task with important applications in the field of remote sensing. The idea of density peaks searching for infrared small target detection has been proved to be effective. However, if high-brightness clutter is close to the target, the distance from the target pixel to the surrounding density peak will be very small, which easily leads to missing detection. In this paper, a new detection method, named modified density peaks searching and local gray difference (MDPS-LGD), is proposed. First, a local heterogeneity indicator is used as the density to suppress high-brightness clutter, and an iterative search is adopted to improve the efficiency in the process of searching for density peaks. Following this, a local feature descriptor named the local gray difference indicator (LGD) is proposed according to the local features of the target. In order to highlight the target, we extract the core area of the density peak by a random walker (RW) algorithm, and take the maximum response of the minimum gray difference element in the core region as the LGD of the density peak. Finally, targets are extracted using an adaptive threshold. Extensive experimental evaluation results in various real datasets demonstrate that our method outperforms state-of-the-art algorithms in both background suppression and target detection.