[1] The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is a hyperspectral imager on the Mars Reconnaissance Orbiter (MRO) spacecraft. CRISM consists of three subassemblies, a gimbaled Optical Sensor Unit (OSU), a Data Processing Unit (DPU), and the Gimbal Motor Electronics (GME). CRISM's objectives are (1) to map the entire surface using a subset of bands to characterize crustal mineralogy, (2) to map the mineralogy of key areas at high spectral and spatial resolution, and (3) to measure spatial and seasonal variations in the atmosphere. These objectives are addressed using three major types of observations. In multispectral mapping mode, with the OSU pointed at planet nadir, data are collected at a subset of 72 wavelengths covering key mineralogic absorptions and binned to pixel footprints of 100 or 200 m/pixel. Nearly the entire planet can be mapped in this fashion. In targeted mode the OSU is scanned to remove most along-track motion, and a region of interest is mapped at full spatial and spectral resolution (15–19 m/pixel, 362–3920 nm at 6.55 nm/channel). Ten additional abbreviated, spatially binned images are taken before and after the main image, providing an emission phase function (EPF) of the site for atmospheric study and correction of surface spectra for atmospheric effects. In atmospheric mode, only the EPF is acquired. Global grids of the resulting lower data volume observations are taken repeatedly throughout the Martian year to measure seasonal variations in atmospheric properties. Raw, calibrated, and map-projected data are delivered to the community with a spectral library to aid in interpretation.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2006JE002682

Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Reconnaissance Orbiter (MRO)

The Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) and Narrow Angle Cameras (NACs) are on the NASA Lunar Reconnaissance Orbiter (LRO). The WAC is a 7-color push-frame camera (100 and 400 m/pixel visible and UV, respectively), while the two NACs are monochrome narrow-angle linescan imagers (0.5 m/pixel). The primary mission of LRO is to obtain measurements of the Moon that will enable future lunar human exploration. The overarching goals of the LROC investigation include landing site identification and certification, mapping of permanently polar shadowed and sunlit regions, meter-scale mapping of polar regions, global multispectral imaging, a global morphology base map, characterization of regolith properties, and determination of current impact hazards.

/pdf/lunar-reconnaissance-orbiter-camera-lroc-instrument-overview-42cq4pmzty.pdf

Lunar Reconnaissance Orbiter Camera (LROC) Instrument Overview

The Hyperion Imaging Spectrometer was the first imaging spectrometer to routinely acquire science-grade data from Earth orbit. Instrument performance was validated and carefully monitored through a combination of calibration approaches: solar, lunar, earth (vicarious) and atmospheric observations complemented by onboard calibration lamps and extensive prelaunch calibration. Innovative techniques for spectral calibration of space-based sensors were also tested and validated. Instrument performance met or exceeded predictions including continued operation well beyond the planned one-year program.

Hyperion, a space-based imaging spectrometer

Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Deep learning classifiers for hyperspectral imaging: A review

Accurate correction of the corrupting effects of the atmosphere and the water’s surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi- and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio–Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

Sensor capability and atmospheric correction in ocean colour remote sensing

Monitoring the Earth's shortwave reflectance with persistent, sub-diurnal, cloud-resolved sampling would improve understanding of key climate system processes and track geo-engineering efforts Current measurement systems do not provide the spatial, spectral or temporal sampling to distinguish between natural variability and anthropogenic modification, so this paper presents Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) system architectures that would provide these monitoring capabilities The LEO system concept would be a constellation of sixteen microsatellites in four orbit planes with a miniature push-broom Dyson spectrometer providing a 709 km swath The satellite would be a Boeing microsat that builds on heritage of recent Boeing Space Environmental NanoSat Experiment (SENSE) mission The GEO system concept would be a scanning Offner spectrometer on a constellation of six satellites launched from a single EELV class launch vehicle Both systems would provide a two hour revisit, globally, at 1 km spatial resolution and 10 nm spectral resolution between 380 nm and 2510 nm

Monitoring Earth's shortwave reflectance: LEO and GEO system architectures

We performed contrast-sensitivity measurements using rotationally symmetric, variable contrast difference-of-Gaussians targets, viewed through specially designed telescopes that presented various amounts of monochromatic aberrations. The contrast-sensitivity ratios between aberrated and unaberrated cases were correlated with instrument-based measures of image quality. The radius that encircles 84% of the energy of the point-spread function was found to give the best correlation with the subjective data.

Visual instrument image-quality assessment with rotationally symmetric difference of Gaussians.

In this paper we present a GEO instrument concept dedicated to monitoring the Earth's global spectral reflectance with a high revisit rate.

Monitoring Earth's shortwave reflectance: GEO instrument concept

The Snow and Water Imaging Spectrometer (SWIS) is a fast, high-uniformity, low-polarization sensitivity imaging spectrometer and telescope system designed for integration on a 6U CubeSat platform. Operating in the 350-1700 nm spectral region with 5.7 nm sampling, SWIS is capable of simultaneously addressing the demanding needs of coastal ocean science and snow and ice monitoring. New key technologies that facilitate the development of this instrument include a linear variable anti-reflection (LVAR) detector coating for stray light management, and a single drive on-board calibration mechanism utilizing a transmissive diffuser for solar calibration. We provide an overview of the SWIS instrument design, spacecraft configuration design, and potential science missions.

Pantazis Mouroulis

Papers

Monitoring Earth's shortwave reflectance: LEO and GEO system architectures

Visual instrument image-quality assessment with rotationally symmetric difference of Gaussians.

Monitoring Earth's shortwave reflectance: GEO instrument concept

Snow and Water Imaging Spectrometer (SWIS): development of a CubeSat-compatible instrument

Ultra Compact Imaging Spectrometer