Flash LiDARs have been described as key components for future space missions including rover operations and the descent and landing of spacecraft. The system records full 3D-images along with 2D intensity images with a single laser pulse, thus eliminating the need for a scanning device. There is a vast literature and well-established techniques concerning radiometric calibration (2D information). However, the illumination of the target is usually considered uniform, which is not the case for increasingly larger focal plane arrays (FPA). In this article we propose a intensity calibration scheme for CEA-LETIs LiDAR prototype in order to compensate the nonuniform illumination of the target and present the first enhanced results based on data acquired under laboratory conditions.

High resolution population count data are vital for numerous applications such as urban planning, transportation model calibration, and population growth impact measurements, among others. In this work, we present and evaluate an end-to-end framework for computing disaggregated population mapping employing convolutional neural networks (CNNs). Using urban data extracted from the OpenStreetMap database, a set of urban features are generated which are used to guide population density estimates at a higher resolution. A population density grid at a 200 by 200 meter spatial resolution is estimated, using as input gridded population data of 1 by 1 kilometer. Our approach relies solely on open data with a wide geographical coverage, ensuring replicability and potential applicability to a great number of cities in the world. Fine-grained gridded population data is used for 15 French cities in order to train and validate our model. A stand-alone city is kept out for the validation procedure. The results demonstrate that the neural network approach using massive OpenStreetMap data outperforms other approaches proposed in related works.

Pushbroom cameras are widely used for earth observation applications. This sensor acquires 1D images over time and uses the straight motion of the satellite to sweep out a region of space and build a 2D image. The stability of the satellite is critical during the pushbroom acquisition process. Therefore its attitude is assumed to be constant overtime. However, the recent manufacture of smaller and lighter satellites to reduce launching cost has weakened this assumption. Small oscillations of the satellite's attitude can result in noticeable warps in images, and geolocation information is lost as the satellite does not capture what it ought to. Current solutions use inertial sensors to control the attitude and correct the images, but they are costly and of limited precision. As the warped images do contain information about attitude variations, we suggest using image registration to estimate them. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. We embed the estimation in a Bayesian framework where image registration, a prior on attitude variations and a radiometric correction model are fused to retrieve the motion of the satellite. We illustrate the performance of our algorithm on four satellite datasets.

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 imagery1.

Satellite image registration has been investigated for several years. Nevertheless, little attention has been paid to the linear geometry of the satellite's imaging sensor, often consisting of several pushbroom cameras. Each pushbroom camera captures 1-D image and uses straight motion of the satellite to build a 2-D image. Yet, attitude variations of the satellite during the aquisition process can lead to significant distortions in the 2-D image. In this paper, we expose the problem and present a constrained image registration method to estimate the satellite's attitude variations, and thus correct the distorted images. We use a Lucas Kanade framework and a piecewise polynomial model under constraints to deduce the registration equation. The performances of our algorithm are shown on two satellite datasets.

Pushbroom sensors are widely used for earth observation from space; this camera captures 1-D image and uses straight motion of the satellite to build a 2-D image. Yet, attitude variations of the satellite during the aquisition process can lead to significant distortions in the 2-D image. Current solutions use inertial sensors to record attitude variations and correct images, but they are expensive and not accurate enough for high frequency variations. In this paper, we expose the problem and present an image registration method to estimate the satellite's attitude variations, and thus correct the distorted images. We use a Lucas Kanade framework to deduce the registration equation; the performances of our algorithm are shown on two satellite datasets.