Herbert E. M. Viggh

Spatial surface prior information reflectance estimation (SPIRE) algorithms

A new set of algorithms has been developed to estimate spectral reflectance in remote sensing imagery. These algorithms are called surface prior information reflectance estimation (SPIRE) algorithms and estimate surface spectral reflectance using prior spatial and spectral information about the surface reflectance. This paper describes SPIRE algorithms that employ spatial processing of single-channel data to estimate local changes in spectral reflectance under spatially and spectrally varying multiplicative and additive noise caused by variations in illumination and atmospheric effects. Rather than modeling the physics of the atmosphere and illumination (using a physics-based code such as the Atmospheric Removal (ATREM) program), or using ground truth spectra at known locations to compensate for these effects [using the empirical line method (ELM)], prior information about the low spatial frequency content of the scene in each spectral channel is used instead. HYDICE visible near-infrared shortwave infrared (VNIR-SWIR) hyperspectral data were used to compare the performance of SPIRE, ATREM, and ELM atmospheric compensation algorithms. The spatial SPIRE algorithm performance was found to be nearly identical to the ELM ground-truth-based results, while spatial SPIRE performed better than ATREM overall and significantly better under high clouds and haze.

Initial asteroid detection results using the Space Surveillance Telescope

The Lincoln Near Earth Asteroid Research (LINEAR) program, funded by the National Aeronautics and Space Administration (NASA), conducted asteroid search from 1998 to 2013 using two 1m optical telescopes at the Massachusetts Institute of Technology Lincoln Laboratory (MITLL) Experimental Test Site (ETS) in Socorro, NM. During this period, the LINEAR program made significant contributions to the discovery of Near-Earth Objects (NEOs), thereby improving knowledge of the NEO size distribution and helping to characterize the threat from NEOs. The LINEAR program has now transitioned to operations using the new 3.5 m wide-field-of-view Space Surveillance Telescope (SST) located at the Atom Site on White Sands Missile Range, NM. The SST was developed for the Defense Advanced Research Projects Agency (DARPA) by MITLL to advance the nations capabilities in space situational awareness. The goals of LINEAR using SST are to continue discovering NEOs, to improve knowledge of the NEO size distribution down to 140 m, and to discover small (2-15 m diameter) NEOs potentially suitable for a NASA asteroid retrieval mission. This paper will describe the capabilities of SST for asteroid search, the strategy for LINEAR search using SST, and the new LINEAR SST processing pipeline. Recent simulation, observing, and detection results will also be presented, along with planned improvements to the system.

Radiometric calibration pipeline for the EO-1 Advanced Land Imager

This paper covers the design and initial performance analysis of the radiometric calibration pipeline software for the EO-1 Advanced Land Imager (EO-1). The design and implementation of the software and of the radiometric calibration database are discussed. Radiometric calibration data were gathered in the laboratory during integration and test and were used for initializing the database. This database will be updated in the future with data collected on orbit. Initial performance results have been obtained after applying the radiometric calibration to imaging data collected during ALI integration and test and ground calibration. During the initial test it was noted a few detectors leaked energy into other detectors, thereby requiring special calibration processing for the affected detectors. The handling of this anomaly is discussed, and the initial performance results of the calibration pipeline with the anomaly corrections will be shown.

Performance assessment software for the EO-1 Advanced Land Imager

The performance assessment of the EO-1 Advanced Land Imager (ALI) requires software for processing data collected during sensor integration and test, ground calibration, and on- orbit operations. This paper describes the software developed for performance assessment processing and analysis of data collected on the ground during ALI and ground calibration. This involves various characterizations and calibrations of the ALI, including functional test, focus, MTF, radiometric response, spectral response, functional image reconstruction, and internal calibration lamp data processing. Processing examples are given, including results. Also, each section describes the use of this software to support the analysis of data collected on-orbit during mission operations, as well as the types of data to be collected and processed. The interface between MIT Lincoln Laboratory and the EO-1 Mission Operations Center at NASA Goddard Space Flight Center is also described.

Automated ground data acquisition and processing system for calibration and performance assessment of the EO-1 Advanced Land Imager

The calibration and performance assessment of the Earth Observing-1 (EO-1) Advanced Land Imager (ALI) required a ground data system for acquiring and processing ALI data. In order to meet tight schedule and budget requirements, an automated system was developed that could be run by a single operator. This paper describes the overall system and the individual Electrical Ground Support Equipment (EGSE) and computer components used. The ALI Calibration Control Node (ACCN) serves as a test executive with a single graphical user interface to the system, controlling calibration equipment and issuing data acquisition and processing requests to the other EGSE and computers. EGSE1, a custom data acquisition syste, collects ALI science data and also passes ALI commanding and housekeeping telemetry collection requests to EGSE2 and EGSE3 which are implemented on an ASIST workstation. The performance assessment machine, stores and processes collected ALI data, automatically displaying quick-look processing results. The custom communications protocol developed to interface these various machines and to automate their interactions is described, including the various modes of operation needed to support spatial, radiometric, spectral, and functional calibration and performance assessment of the ALI.

Composable applications using service encapsulation (CAUSE)

Intelligence Surveillance and Reconnaissance (ISR) systems have the potential to operate in a net-centric environment in which tasking control, collected sensor data, and intelligence feeds can be made available via web services under a service oriented architecture. Rapid composition of ISR applications from such services would enable analysts and warfighters to quickly adapt to changing missions and ISR system availability. This paper describes a composition approach in which plug-ins that encapsulate service interactions are used to build applications. This encapsulation allows for heterogeneous service technologies to be used in an application. A composition Graphical User Interface (GUI) allows the user to discover needed service plug-ins, connect outputs to inputs between services, and generate a consolidated application GUI. Semantic descriptions of the plug-ins and their inputs and outputs facilitate consistency checks that ensure proper operation of the composed application. An initial implementation of such an application composition architecture was developed and demonstrated with simulated ISR assets and data feeds.

Air Force Planetary Defense System: Initial Field Test Results

Over the past several years, the Air Force has been developing new devices and technology for the detection and tracking of earth orbiting satellites. This technology has been targeted to provide an upgraded capability for an operational space surveillance system called GEODSS. Currently, a number of GEODSS systems are deployed around the world as part of the world-wide space surveillance system operated by the US Air Force. Each GEODSS site is currently equipped with 1-meter class telescopes and EBSICON detector systems which represent 1970s technology. The Air Force is now in the process of upgrading the GEODSS system to achieve the performance offered by state of the art detector systems. Under Air Force sponsorship, Lincoln Laboratory has developed a new generation of sensitive, large format, frame transfer CCD focal planes for GEODSS. These focal planes have been installed in a new generation of cameras and are currently undergoing testing at the Lincoln Laboratory Experimental Test Site (ETS). When equipped with the new focal plane and camera technology, the modest sized GEODSS telescopes have considerable capability to conduct large coverage, sensitive searches for earth crossing asteroids. Theoretical analysis has indicated that the CCD equipped GEODSS telescope will be capable of achieving a limiting magnitude of 22, over a 2 sq/deg field of view, with about 100 seconds of integration. This is comparable to the sensitivity of considerably larger telescopes equipped with current cameras. In addition to the high sensitivity, the CCD is configured for frame transfer operations which are well suited to asteroid search operations. This paper will present the results of the initial system tests conducted at the ETS and will discuss how this technology fits into a concept of operations for a planetary defense system based on the Air Force developed technology.

Surface Reflectance Estimation Using Prior Spatial and Spectral Information

Surface prior-information reflectance estimation (SPIRE) algorithms estimate changes in spectral reflectance using imperfect prior spatial and spectral information. This paper combines spectral and spatial processing to estimate local changes in spectral reflectance between pairs of spectral images under spatially and spectrally varying multiplicative and additive noise, which arise from variations in illumination and atmospheric effects. This approach extends the spatial SPIRE algorithms that were described earlier and utilizes only a prior reflectance image cube and ensembles of typical multiplicative and additive illumination noise spectral vectors that are deduced from images cubes of similar scenes. The method minimizes the impact of environmental noise by replacing with their prior equivalents low-spatial-frequency content and low-order principal components that are known to be noisy based on prior noise spectra. This filtering and substitution process occurs in log space when minimizing the effects of multiplicative noise. Tests on Hyperspectral Digital Imagery Collection Experiment visible near-infrared-shortwave infrared data demonstrated the algorithms superior ability to estimate absolute reflectance changes under varying illumination conditions. SPIRE performance was nearly identical to the empirical line method (ELM) ground-truth-based atmospheric compensation results and was better than the physics-based Atmospheric removal (ATREM) code overall, particularly, under high clouds and haze. A ldquoSelective SPIRErdquo technique that chooses between combined-spatial/spectral and spectral-only SPIRE reflectance estimates was developed; it maximizes estimation performance on both changed and unchanged pixels. Minimum-distance classification experiments demonstrated Selective SPIREs superior performance relative to both ATREM and ELM in cross-image supervised classification applications.

Improving object detection in 2D images using a 3D world model

A mobile robot operating in a netcentric environment can utilize offboard resources on the network to improve its local perception. One such offboard resource is a world model built and maintained by other sensor systems. In this paper we present results from research into improving the performance of Deformable Parts Model object detection algorithms by using an offboard 3D world model. Experiments were run for detecting both people and cars in 2D photographs taken in an urban environment. After generating candidate object detections, a 3D world model built from airborne Light Detection and Ranging (LIDAR) and aerial photographs was used to filter out false alarm using several types of geometric reasoning. Comparison of the baseline detection performance to the performance after false alarm filtering showed a significant decrease in false alarms for a given probability of detection.