Earth Observation via Compressive Sensing: The Effect of Satellite Motion

Luca Oggioni,David Sanchez Del Rio Kandel,Giorgio Pariani

doi:10.3390/rs14020333

Abstract

In the framework of earth observation for scientific purposes, we consider a multiband spatial compressive sensing (CS) acquisition system, based on a pushbroom scanning. We conduct a series of analyses to address the effects of the satellite movement on its performance in a context of a future space mission aimed at monitoring the cryosphere. We initially apply the state-of-the-art techniques of CS to static images, and evaluate the reconstruction errors on representative scenes of the earth. We then extend the reconstruction algorithms to pushframe acquisitions, i.e., static images processed line-by-line, and pushbroom acquisitions, i.e., moving frames, which consider the payload displacement during acquisition. A parallel analysis on the classical pushbroom acquisition strategy is also performed for comparison. Design guidelines following this analysis are then provided.

Highlights

Imaging spectroscopy is an effective tool, whose use is growing enormously in recent years, able to acquire spatially, and spectrally resolved images
We perform a Fourier analysis, which is useful to highlight the differences in the reconstruction accuracy both along track and across track, and we show the ratio between the 2D fast Fourier transformations (FFT)
With the goal to evaluate the feasibility of a spatial multispectral pushbroom acquisi4

Summary

Introduction

Imaging spectroscopy is an effective tool, whose use is growing enormously in recent years, able to acquire spatially, and spectrally resolved images. Nowadays, several space missions equipped with hyperspectral sensors are orbiting earth, which are devoted to different applications encompassing land, ocean, and atmospheric components [8,9,10,11,12,13,14] In this framework, there is a strong driving force of finding new and alternative acquisition techniques with the idea of simplifying in size and complexity the acquisition hardware and the amount of data to be collected and transferred. CS applies linear projections in the optical domain such that this dimensionality reduction occurs simultaneously with image sensing and acquisition This brings about the capability of reducing the sampling operations at the sensory level, with the counterbalance of a computation effort in retrieving the original information [17,22]. After the demonstration of the single-pixel camera (SPC) in a restricted

Methods

Results

Conclusion