Land and ocean data product generation from visible-through-shortwave-infrared multispectral and hyperspectral
imagery requires atmospheric correction or compensation, that is, the removal of atmospheric absorption and scattering
effects that contaminate the measured spectra. We have recently developed a prototype software system for automated,
low-latency, high-accuracy atmospheric correction based on a C++-language version of the Spectral Sciences, Inc.
FLAASH™ code. In this system, pre-calculated look-up tables replace on-the-fly MODTRAN® radiative transfer
calculations, while the portable C++ code enables parallel processing on multicore/multiprocessor computer systems.
The initial software has been installed on the Sensor Web at NASA Goddard Space Flight Center, where it is currently
atmospherically correcting new data from the EO-1 Hyperion and ALI sensors. Computation time is around 10 s per
data cube per processor. Further development will be conducted to implement the new atmospheric correction software
on board the upcoming HyspIRI mission's Intelligent Payload Module, where it would generate data products in nearreal
time for Direct Broadcast to the ground. The rapid turn-around of data products made possible by this software
would benefit a broad range of applications in areas of emergency response, environmental monitoring and national
A composite signature is a group of signatures that are related in such a way to more completely or further define a
target or operational endeavor at a higher fidelity. This paper explores the merits of using composite signatures, in lieu
of waiting for opportunities for the more elusive diagnostic signatures, to satisfy key essential elements of information
Keywords: signature, composite signature, civil disaster
(EEI) associated with civil disaster-related problems. It discusses efforts to refine composite signature development
methodology and quantify the relative value of composite vs. diagnostic signatures. The objectives are to: 1) investigate
and develop innovative composite signatures associated with civil disasters, including physical, chemical and
pattern/behavioral; 2) explore the feasibility of collecting representative composite signatures using current and
emerging intelligence, surveillance, and reconnaissance (ISR) collection architectures leveraging civilian and
commercial architectures; and 3) collaborate extensively with scientists and engineers from U.S. government
organizations and laboratories, the defense industry, and academic institutions.
This paper describes the work being managed by the NASA Goddard Space Flight Center (GSFC) Information System
Division (ISD) under a NASA Earth Science Technology Office (ESTO) Advanced Information System Technology
(AIST) grant to develop a modular sensor web architecture which enables discovery of sensors and workflows that can
create customized science via a high-level service-oriented architecture based on Open Geospatial Consortium (OGC)
Sensor Web Enablement (SWE) web service standards. These capabilities serve as a prototype to a user-centric
architecture for Global Earth Observing System of Systems (GEOSS). This work builds and extends previous sensor
web efforts conducted at NASA/GSFC using the Earth Observing 1 (EO-1) satellite and other low-earth orbiting
As more assets are placed in orbit, opportunities emerge to combine various sets of satellites in temporary constellations to perform collaborative image collections. Often, new operations concepts for a satellite or set of satellites emerge after launch. To the degree with which new space assets can be inexpensively and rapidly integrated into temporary or "ad hoc" constellations, will determine whether these new ideas will be implemented or not. On the Earth Observing 1 (EO-1) satellite, a New Millennium Program mission, a number of experiments were conducted and are being conducted to demonstrate various aspects of an architecture that, when taken as a whole, will enable progressive mission autonomy. In particular, the target architecture will use adaptive ground antenna arrays to form, as close as possible, the equivalent of wireless access points for low earth orbiting satellites. Coupled with various ground and flight software and the Internet, the architecture enables progressive mission autonomy. Thus, new collaborative sensing techniques can be implemented post-launch. This paper will outline the overall operations concept and highlight details of both the research effort being conducted in the area of adaptive antenna arrays and some of the related successful autonomy software that has been implemented using EO-1 and other operational satellites.
Recent autonomy experiments conducted on Earth Observing 1 (EO-1) using the Autonomous Sciencecraft Experiment (ASE) flight software has been used to classify key features in hyperspectral images captured by EO-1. Furthermore, analysis is performed by this software onboard EO-1 and then used to modify the operational plan without interaction from the ground. This paper will outline the overall operations concept and provide some details and examples of the onboard science processing, science analysis, and replanning.
A cloud cover detection algorithm was developed for application to EO-1 Hyperion hyperspectral data. The algorithm uses only bands in the reflected solar spectral regions to discriminate clouds from surface features and was designed to be used on-board the EO-1 satellite as part of the EO-1 Extended Mission Phase of the EO-1 Science Program. The cloud cover algorithm uses only 6 bands to discriminate clouds from other bright surface features such as snow, ice, and desert sand. The code was developed using 20 Hyperion scenes with varying cloud amount, cloud type, underlying surface characteristics and seasonal conditions. Results from the application of the algorithm to these test scenes is given with a discussion on the accuracy of the procedure used in the cloud cover discrimination. Compared to subjective estimates of the scene cloud cover, the algorithm was typically within a few percent of the estimated total cloud cover.