Given the recent development in high-resolution (HR) optical satellites, the study of both attitude jitter (AJ) detection and compensation has become increasingly essential to improving the radiometric and geometric quality of HR images. A group of HR optical stereo mapping satellites in China, mapping satellite-1 (MS-1) has launched two satellites and will launch one satellite to build a satellite network. The geometric accuracy of the launched MS-1 satellites is greater than 80 m because of the AJ caused by the instability of the platform. AJ detection and compensation are critical issues that must be addressed to improve the accuracy of geo-positioning and mapping before launching a new satellite. The present study employs a method of jitter detection based on short-time asynchronous images to detect MS-1 jitter. The adjacent overlapping areas of an original panchromatic image are used as detection images instead of the traditional multispectral images, and a differential recursion optimal estimation filter is proposed for the optimal estimation and elimination of the gross errors of the registration data procedure, thereby increasing the detection accuracy. The space variant blurring model and viewing angles correction method are employed for the radiometric and geometric jitter compensation of images, respectively. The methods of radiometric objective evaluation indices and geometric checkpoint are then utilised to evaluate the quality of jitter compensation. Finally, the DeZhou regional image (ShanDong province, China) from MS-1 is used as the experimental data. Results for the AJ of MS-1 are analysed and reported for the first time. The assessment results obtained show that both radiometric and geometric qualities greatly increase after the jitter compensation procedure. Thus, the work of this study for jitter detection and compensation effectively addresses the jitter of MS-1 HR optical satellites.

Multi-digital camera systems (MDCS) are constantly being improved to meet the increasing requirement of high-resolution spatial data. This study identifies the insufficiencies of traditional MDCSs and proposes a new category MDCS based on tilt-shift photography to improve ability of the MDCS to acquire high-accuracy spatial data. A prototype system, including two or four tilt-shift cameras (TSC, camera model: Nikon D90), is developed to validate the feasibility and correctness of proposed MDCS. Similar to the cameras of traditional MDCSs, calibration is also essential for TSC of new MDCS. The study constructs indoor control fields and proposes appropriate calibration methods for TSC, including digital distortion model (DDM) approach and two-step calibrated strategy. The characteristics of TSC are analyzed in detail via a calibration experiment; for example, the edge distortion of TSC. Finally, the ability of the new MDCS to acquire high-accuracy spatial data is verified through flight experiments. The results of flight experiments illustrate that geo-position accuracy of prototype system achieves 0.3 m at a flight height of 800 m, and spatial resolution of 0.15 m. In addition, results of the comparison between the traditional (MADC II) and proposed MDCS demonstrate that the latter (0.3 m) provides spatial data with higher accuracy than the former (only 0.6 m) under the same conditions. We also take the attitude that using higher accuracy TSC in the new MDCS should further improve the accuracy of the photogrammetry senior product.

Given the recent development of agile optical satellites for rapid-response land observation, side-oblique image-motion (SOIM) detection and blur correction have become increasingly essential for improving the radiometric quality of side-oblique images. The Chinese small-scale agile mapping satellite Kuaizhou-1 (KZ-1) was developed by the Harbin Institute of Technology and launched for multiple emergency applications. Like other agile satellites, KZ-1 suffers from SOIM blur, particularly in captured images with large side-oblique angles. SOIM detection and blur correction are critical for improving the image radiometric accuracy. This study proposes a SOIM restoration method based on segmental point spread function detection. The segment region width is determined by satellite parameters such as speed, height, integration time, and side-oblique angle. The corresponding algorithms and a matrix form are proposed for SOIM blur correction. Radiometric objective evaluation indices are used to assess the restoration quality. Beijing regional images from KZ-1 are used as experimental data. The radiometric quality is found to increase greatly after SOIM correction. Thus, the proposed method effectively corrects image motion for KZ-1 agile optical satellites.

Imagery geometry models (IGMs) of the high-resolution satellite images (HRSIs) are always of great interest in the photogrammetry and remote sensing community for the raising new kinds of sensors and imaging systems. Especially the generalized sensor models (GSMs) have been widely used for positioning of satellite images, and the accuracy are already validated. Since Back propagation (BP) neural network is a better choice for the two key reasons of the replacement of physical sensor models by generalized sensor models, numerous mathematical estimations for every specialized sensor, and secret equations of the IGMs. Experiments are carried out to test the approximation accuracy of the new generalized sensor model. And the experimental results show that, the BP neural network is of extremely high accuracy for satellite imagery photogrammetric restitution.