Visual Analytics of High-dimensional Data Sets: A Hyperspectral Imagery Test Case

Visualization and interpretation of big data poses new and unique challenges. As engineering students enter the work force, many will be tasked with analyzing increasingly large and complex data sets with which they have little experience. This paper presents simple heat map and multi-line plotting techniques used to select critical spectral attributes produced from data mining a hyperspectral satellite image for bathymetry mapping. Additionally, good graphic design practices regarding color choice and reducing visual distraction are suggested in order to more quickly and clearly communicate information to an audience. These techniques can be applied to all types of data visualization as an effective way of communicating data

Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site)

Models based on the multivariate partial least squares (PLS) regression technique are developed for the retrieval of phytoplankton size structure from measured light absorption spectra (BOUSSOLE site, northwestern Mediterranean Sea). PLS-models trained with data from the Mediterranean Sea showed good accuracy in retrieving, over the nine-year BOUSSOLE time series, the concentrations of total chlorophyll a [Tchl a], of the sum of seven diagnostic pigments and of pigments associated with micro, nano, and picophytoplankton size classes separately. PLS-models trained using either total particle orphytoplankton absorption spectra performed similarly, and both reproduced seasonal variations of biomass and size classes derived by high performance liquid chromatography. Satisfactory retrievals were also obtained using PLS-models trained with a data set including various locations of the world’s oceans, with however a lower accuracy. These results open the way to an application of this method to absorption spectra derived from hyperspectral and field satellite radiance measurements

GAL: A Stepwise Model for Automated Cloud Shadow Detection in HICO Oceanic Imagery Utilizing Guided Filter, Pixel Assignment, and Geometric Linking

Detection of cloud shadow pixels is an important step in image processing in several remote sensing ocean-color application domains, such as obtaining chlorophyll content. While shadow detection algorithms do exist, the vast majority are for over land which leaves few options for detection over water. The detection of cloud shadow over water in HICO imagery is a unique problem. As its name implies, HICO (Hyperspectral Imager for the Coastal Ocean) imagery is produced for coastal and oceanic regions. Since land based algorithms remove water before processing, these approaches would not be applicable. The only currently published HICO shadow pixel detection algorithm [1] produces good results for predominantly homogeneous regions. It also involves hand-tuning of the parameters, which is not suitable for automation. GAL is a fully automated stepwise model that starts by using satellite imagery and navigational data. The next step is applying the guided filter algorithm proposed by He, Sun, and Tang [2] to these images in order to filter and enhance the images before shadow detection. The third step classifies pixels into water, land, and clouds. The fourth step uses cloud shadow geometry to indicate possible shadow pixels. The final step is to reduce the amount of possible shadow pixels to the most probable shadow pixels. This research combines the past techniques of cloud shadow geometry, edge detection, and thresholding, along with the new techniques of guided image filtering, in such a way that has never been done before. GAL works best with well-defined cloud shadows that contain a large contrast between water and shadow. Water type, coastal or deep ocean, does not affect GAL. Shadows with a large gradient may be under-detected. GAL can be applied to HICO data immediately, with the potential of being applied to all global high resolution ocean-color satellite imagery

Atmospheric Correction for Hyperspectral Ocean Color Retrieval with Application to the Hyperspectral Imager for the Coastal Ocean (HICO)

The classical multi-spectral Atmospheric Correction (AC) algorithm is inadequate for the new generation of spaceborne hyperspectral sensors such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and scattering by aerosols from the measured top-of-atmosphere (TOA) radiance, compensate for the absorption by atmospheric gases, and correct for reflection and refraction of the air-sea interface. In this work, we present and evaluate an improved AC for hyperspectral sensors developed within NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System software package (SeaDAS). The improvement is based on combining the classical AC approach of multi-spectral capabilities to correct for the atmospheric path radiance, extended to hyperspectral, with a gas correction algorithm to compensate for absorbing gases in the atmosphere, including water vapor. The SeaDAS-hyperspectral version is capable of operationally processing the AC of any hyperspectral airborne or spaceborne sensor. The new algorithm development was evaluated and assessed using the Hyperspectral Imager for Coastal Ocean (HICO) scenes collected at the Marine Optical BuoY (MOBY) site, and other SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and AERosol Robotic NETwork - Ocean Color (AERONET-OC) coastal sites. A hyperspectral vicarious calibration was applied to HICO, showing the validity and consistency of HICO's ocean color products. The hyperspectral AC capability is currently available in SeaDAS to the scientific community at https://oceancolor.gsfc.nasa.gov/

Analysis of Slewing and Attitude Determination Requirements for CTEx

This thesis examines the slewing and attitude determination requirements for the Chromotomographic Experiment (CTEX), a chromotomographic-based hyperspectral imager, to be mounted on-board the Japanese Experiment Module (JEM) External Facility (EF). The in-track slewing requirement is driven by the facts that CTEx has a very small field of view (FOV) and is required to collect 10 seconds of data for any given collection window. The need to slew in the cross-track direction is a product of the small FOV and target/calibration site access. CTEx incorporates a two-axis slow-steering dwell mirror with a range of ± 8 degrees and an accuracy of 10 arcseconds in each axis to slew the FOV. The inherent inaccuracy in the knowledge of the International Space Station\u27s (ISS) attitude (± 3 degrees) poses significant complications in accurately pointing CTEx even with more accurate (0.3 degrees) attitude information provided by the JEM. The desire is for CTEx to incorporate a star tracker with 1 arcsecond accuracy to determine attitude without reliance on outside sources

PACE Technical Report Series, Volume 6: Data Product Requirements and Error Budgets Consensus Document

This chapter summarizes ocean color science data product requirements for the Plankton, Aerosol, Cloud,ocean Ecosystem (PACE) mission's Ocean Color Instrument (OCI) and observatory. NASA HQ delivered Level-1 science data product requirements to the PACE Project, which encompass data products to be produced and their associated uncertainties. These products and uncertainties ultimately determine the spectral nature of OCI and the performance requirements assigned to OCI and the observatory. This chapter ultimately serves to provide context for the remainder of this volume, which describes tools developed that allocate these uncertainties into their components, including allowable OCI systematic and random uncertainties, observatory geo location uncertainties, and geophysical model uncertainties

A unified approach to estimate land and water reflectances with uncertainties for coastal imaging spectroscopy

Coastal ecosystem studies using remote visible/infrared spectroscopy typically invert an atmospheric model to estimate the water-leaving reflectance signal. This inversion is challenging due to the confounding effects of turbid backscatter, atmospheric aerosols, and sun glint. Simultaneous estimation of the surface and atmosphere can resolve the ambiguity enabling spectral reflectance maps with rigorous uncertainty quantification. We demonstrate a simultaneous retrieval method that adapts the Optimal Estimation (OE) formalism of Rodgers (2000) to the coastal domain. We compare two surface representations: a parametric bio-optical model based on Inherent Optical Properties (IOPs); and an expressive statistical model that estimates reflectance in every instrument channel. The latter is suited to both land and water reflectance, enabling a unified analysis of terrestrial and aquatic domains. We test these models with both vector and scalar Radiative Transfer Models (RTMs). We report field experiments by two airborne instruments: NASA's Portable Remote Imaging SpectroMeter (PRISM) in an overflight of Santa Monica, California; and NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) in an overflight of the Wax Lake Delta and lower Atchafalaya River, Louisiana. In both cases, in situ validation measurements match remote water-leaving reflectance estimates to high accuracy. Posterior error predictions demonstrate a closed account of uncertainty in these coastal observations

Design Analysis of a Sapce Based Chromotomographic Hyperspectral Imaging Experiment

This research develops the design of several components and/or systems for an experimental space-based chromotomographic hyperspectral imager that is being built by the Air Force Institute of Technology. The design work includes three separate topics. The first topic was the development of a structure utilizing finite element analysis and eigenanalysis for the ground-based version of the chromotomographic experiment (CTEx). The ground-based experiment was performed as a risk mitigation measure for the space-based experiment. The second topic includes a design review of a contractor\u27s proposed off-axis Mersenne telescope for the space-based chromotomographic hyperspectral imager. The work included the creation of preliminary verification requirements from the contract and sub- sequent analysis of the telescope design based on those requirements. The third topic addressed was a trade study of on-orbit focus, alignment, and calibration schemes for the space-based version of CTEx. The selected imaging focusing method entails imaging Earth-based sodium lights at night while stepping through several focus settings. The optimal focus setting shows the clearest sodium spectral features. The critical alignment concerns were identified as the alignment of the prism and of the collimated light onto the prism. The space-based CTEx utilizes three separate calibration methods involving vicarious Earth-based targets, and on-board laser diodes and spectral filters. The results of the research varied by topic. For the first topic, a structural assembly was successfully fabricated that allowed the goals of the ground-based CTEx to be met, validating the design approach. The design review for the second topic was successful with the contractor\u27s telescope design currently undergoing fabrication with delivery in May 2010. For the third topic, applicable methods and procedures were developed for the space-based CTEx