Estimating satellite attitude from pushbroom sensors

Abstract

Linear pushbroom cameras are widely used in passive remote sensing from space as they provide high resolution images. In earth observation applications, where several pushbroom sensors are mounted in a single focal plane, small dynamic disturbances of the satellite's orientation lead to noticeable geometrical distortions in the images. In this paper, we present a global method to estimate those disturbances, which are effectively vibrations. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. To do so, we embed the estimation process in a Bayesian framework. An autoregressive model is used as a prior on the vibrations. The problem can be seen as a global image registration task where multiple pushbroom images are registered to the same coordinate system, the registration parameters being the vibration coefficients. An alternating maximisation procedure is designed to obtain Maximum a Posteriori estimates (MAP) of the vibrations as well as of the autoregressive model coefficients. We illustrate the performance of our algorithm on various datasets of satellite imagery 1 .

Estimation of an Observation Satellite’s Attitude Using Multimodal Pushbroom Cameras

“…To speed up its convergence, we first run M1 to get an attitude variations estimate which is then used as an initial value for the algorithm. M6 Method 6 has been described in the CVPR paper [30]; it uses the same likelihood term as M1 for image registration. It does not take care of radiometric differences between images, but it has a more specific prior for the attitude variations with an autoregressive model.…”

“…All the three terms depend on the hyperparameters according to Equations (16), (20), (24), (26), and (30). In practice, the gradient of the previous equation can be computed so that a gradient based optimization method can give a solution.…”

“…It is possible to design higher order models [29], [30], but learning the autoregressive coefficients is challenging. Moreover, it is difficult to understand how well such a model generalizes during the lifetime of a satellite.…”

Band co-registration modeling of LAPAN-A3/IPB multispectral imager based on satellite attitude

“…Same as other multispectral satellite images, raw images captured from LAPAN-A3/IPB satellite multispectral imager suffer from both geometric and radiometric distortion which should be systematically corrected. There are several distortions which commonly occur on satellite multispectral images, most importantly are radiometric vignetting distortion [2] and geometric distortion due to satellite movement instability while imaging [3] [4]. Another significant geometric distortion which also occurs on LAPAN-A3/IPB multispectral images is band co-registration distortion, which is caused by position and orientation differences of band detectors respect to lens center [5].…”