No abstract

Abstract. Recent constellations of small satellites, such as Planet’s SkySats, offer new acquisition modes where very short videos or bursts of images are acquired instead of a single still image. Compared to sequences of multi-date images, these sequences of consecutive video frames yield a large redundancy of information within the range of seconds. This redundancy enables to increase the spatial resolution using multi-frame super-resolution algorithms. In this paper, we propose a novel super-resolution method based on a high-order spline interpolation model that combines multiple low-resolution frames to produce a high-resolution image. Moreover this method can be implemented efficiently on GPU to process entire images from real satellite acquisitions. Synthetic and real experiments show that the proposed method is able to recover fine details, and measurements of the resulting resolution indicate a gain of 10 cm / pixel with respect to Planet’s SkySat standard imagery products.

Many blind image deblurring methods rely on unnatural image priors that are explicitly designed to restore salient image structures, necessary to estimate the blur kernel. In this article, we analyze the blur kernel estimation method introduced by Pan and Su in 2013 that uses an 0 prior on the gradient image. We present deconvolution results using the estimated blur kernels. Our experiments show the effectiveness of the method as well as some of its shortcomings. Source Code The C++ source code, the code documentation, and the online demo are accessible at the IPOL web page 1 of this article. Compilation and usage instruction are included in the README.txt file of the archive.

New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities. However, the large volumes of images that they produce have uneven qualities, creating the need for automatic quality assessment methods. In this work, we quantify the sharpness of images from the PlanetScope constellation by estimating the blur kernel from each image. Once the kernel has been estimated, it is possible to compute an absolute measure of sharpness which allows to discard low quality images and deconvolve blurry images before any further processing. The method is fully blind and automatic, and since it does not require the knowledge of any satellite specifications it can be ported to other constellations.