promise of spectral and signatures understanding

Promise of Spectral and Signatures Understanding

Todd Hawley Sean Acklam (SpecTIR)Technical Director Signatures Technology FellowNational Signatures Program National Signatures Program

03 April 2007 Todd Hawley 2

Promise of Spectral

New sensor systems Novel spectral analysis Geo-database population Imagery fusion Information visualization

Real Life Applications Real Life Applications


Biomass Overlay

Relative Biomass

High

Low


Relative Turbidity

Relative Turbidity

Low

High


Bio-Mass / Material Discrimination Study

Wildland Vegetation Density Analysis(determine fuel availability for wildfire and vegetation types)

Natural RGB Density Map Combined

Hyperspectral data enables automated identification of roof types including discrimination of red asphalt shingles from terra cotta roofs.


Urban/Vegetative Boundary

Vegetation, permeability, and soil moisture mapping - Colorado Springs, CO


Wetlands

Principal Component - Unsupervised ClassificationForested Wetlands – MD Eastern Shore – February 2006


Geologic/Energy

Mineralogy Geothermal Oil/gas exploration Mining remediation


Superfund Site


Land Use

Forest fire projections Fuel abundance mapping

Invasive species Land cover/land use

Nutrient value Agriculture/stock Urban mapping Impervious surfaces


AG Site in Mid-West

0.5 meter spatial5 nm spectral

Mosaic of two lines


Road Analysis Results

SpecTIR Proprietary

Correct: Recently paved

Correct: half of road is degraded

Correct: Severely degraded road

Correct: Moderate road with slight cracks


HSI & LIDAR IntegrationAdding unique elements of hyperspectral imaging and material classification…

…to LIDAR-derived topographic, very high resolution topographic information, yields unprecedented level of terrain information


HSI & LIDAR Integration

Paved Asphalt (Streets)Paved Asphalt / Gravel (Lots)

Tar RoofVegetationSandy SoilsMetallic Roofs


Natural DisasterPreliminary analysis of spectral anomalies associated with hurricane damage.Preliminary analysis of spectral anomalies associated with hurricane damage.


Paper IndustryNear InfraRed (NIR) spectral camera together with multiple fiber optics is used to acquire snaphot moisture profiles across paper web in paper machine.


Textile Dyeing

A four-point fiber optic spectrometer measures dyed color at a resolution of <0.2 DE.

Pictures: Coltex system by Iris DP


Pharmaceutical Industry

NIR spectral imaging expands the capabilities of single point near infrared spectrometry to fully cover the material and product streams under inspection Inspection of chemical

composition and its homogenity Detection of foreign pills


GDB Product Examples

• Airfield threat assessments• Airfield line diagrams• Airfield graphics• Airfield image maps• Landing zones• Helicopter landing zones

Airfield Products

• 3D visualization• 3D flythroughs• Elevation tints• Line of sight/intervisibility• Anaglyphs• Viewsheds• Interferomograms• Foliage penetration• Time sequencing• Video feeds

3\4 Dimensional Products

•Littoral studies•Threat predictions•Predictive modeling•Damage assessments•Turbidity•Ports of entry•Beach landing zones

Hydrological Products



• Power distribution• Water distribution• Sewage distribution• Petroleum distribution• Utility isolation• Damage assessments• Threat prediction• Lines of communication• Communication studies• City construction/public works

Engineering Products•R&S reporting graphics•Key infrastructure•Trends & tactics•Predictive modeling•Threat predictions• Damage assessments•Crowd control•Event security•Dignitary security detail •Raid graphics

Domestic Security •Geological studies•Vegetation studies•Environmental hazards/impacts•Terrain categorization•Littoral studies•Geophysical studies

Earth Sciences



•Route analysis•Road isolation•Choke points•Bridges/ ford/ tunnel studies•Trafficability•Traffic Rate•MCOO/ COO•Route studies

Mobility Products

•Urban – orientation•City graphic•City image map•Targeting map•Gridded matrix•Geo – orientation•Change detection

Orientation Products


GDB Visualization


Network Analysis


Data FusionWavelet Fusion Tool: The wavelet fusion tool developed for the STF transforms any geographically linked data into wavelet space (sparse transformation) thereby decorrelating their coefficients, applies a fusion rule to the transformed data sets (dependent on internal geometry), and performs an inverse wavelet transformation on the newly fused datasets. The outcome is a fused dataset independent of wavelength and platform.

Fusion Rule

Inverse Wavelet Transform

Wavelet TransformInput Data


Data Fusion

Independent Component Analysis Tool: The ICA tool uses fused data and transforms into a space where components within the datasets can be isolated for statistical independence from the rest of the dataset. Non independent data like Gaussian noise is “ignored” through the use of negentropy approximations as opposed to kurtosis during the transformation process. These independent components (or vectors) act as unique and accurate signatures for any future classification and feature extraction.


Data Fusion

Generalized Relevance Learning Vector Quantization Tool: The GRLVQ tool is a hybrid classification driven feature extraction that uses the input independent components to classify, discriminate and\or identify features of interest and extract the features into a geo-database as a geographic information system. The final geo-database format, containing all inclusive signatures of the urban area of interest, can be used for a wide variety of analyses and products.


Information Visualization

Axis: Temporal Spatial Wavelength Energy

Unit: Time Meters Nanometers J\GHz

Three Modules for Information Visualization: Three modules define the signatures visualization tool. The first module is data input from the absolute geometric database. Data is represented in the form of clouds and is categorized via six different relationship types. These data clouds are projected using the second module made up of four axes depicted below. Finally, collection gaps and complete signature coverage are visualized by depicting collection asset coverage over the data clouds projected using the four axes.


Signature Visualization Tool (Exemplar)

Data relating to any sensor\observable\event can be loaded into the SVT through XML.

Axis

DataSignatures

Spectral Signatures

Behavior Signatures

RADAR Signatures

TTP signatures

IR Signatures

1.

2.

3.

4.

Temporal

Spatial

Wavelength

Energy


National Signatures Program

Platform for analysis & decisionsMulti-community participation Unified access to diverse, distributed signaturesOperation on classified networks

What DoesNSP Provide?

What isNSP?

Defense Intelligence AgencyNational Ground Intelligence CenterData providersSenior steering group

Who are Key Players?

UsersUsers

Secure Networks

Web-Based Operation

Spectral InfraredInfrared

Acoustic Data

Radar Data

MultipleProviders

DiverseSignatures

Broad-based programto improve signature

management & application


Signatures Features characterize targets, threats, …

Unique, consistently reoccurring Multiple signature domains

Traditional (radar, radio frequency, electro-optical, geophysical, nuclear, materials)

New domains (e.g., chemicals) Multiple data types

Measurements, computational predictions Spectral, time series, images, etc. Spectral Infrared

Infrared Image

Acoustic Data

Radar Data


Objective

User perspective Simple to use one stop shopping Common view of the nation’s signature pool data Clear, definitive search results Downloadable real data

Provider perspective Provide data quickly & efficiently to many users Maintain visibility as the source for hosted data Control data content and quality Define and control data access

Improve signature management and application by balancing data users’ and providers’ needs.


National Signature Pool

Acoustic IR Radar SeismicMS/HSI ChemicalBiologicaletc.

Facilities Mat’ls etc.ShipsVehicles Aircraft Chem’s

Other US Government

Modeling & SimulationIntelligenceTest &

EvaluationOperationsData Need

Radar IR Acoustic Chem etc.

VehiclesAircraft

ShipsFacilities

Target

BioDomain

Provider Provider Provider

NationalSignature Pool

NSP NSP Multiple Providers

Common View

UserCommunities


Operational Overview

Signature ProvidersNSP

LocalDatabase

SignatureFiles

LocalMetadata

DataSet

Target Spect. etc.

1234

X

Y

Z

X

12

5

X

R

S

T

3 U

Metadata

Files

NSPCommon

DC Tgt Spct etc.

ABCC

xxxyyy

zzz

xxx

8-123-5

X

xyz

zyx

yxz

3-5 xyz

Community Wide

Metadata

NSPWeb-BasedApplication

UsersUsers

Find

Retrieve

POC

TargetDataLocationDate

John Doe123-123-1234

Data Summary

XYZIRA

6/1/01

FileDownload

Results

Dynamically GeneratedXML Data Summaries

DescriptionsThumbnail ImagesFile Downloads

(Metadata)(when available)(Signature data)

Data

Data Loading Data Location/Retrieval


Key Components

Dynamic signature operations: immediate on-demand access to all sources of quality assured standardized signatures and related data maintained with DOD, IC, and OGA to support sensor reprogramming in a highly fluid environment

Signature support plan (SSP): Potential observable signature types associated with each critical element of an activity, event, or equipment withing a specific mission area

Operational signature package (OSP): End user defined selection of operational signatures, their specific ordered integration, and the desired time sequencing required to support a specific mission area

Signature Based Tip-off/Cross Cueing

Signature BasedDirect Reporting

Machine-to-Machine Signature Exchanges


SSP/OSP Development

Gather information Identify friendly aircraft signature requirements

Sensor and technique used Required signature fidelity and format

Check existing signature holdings Draft

Sensor specific, enemy aircraft OSP Add required signatures to NSP holdings, or Generate requirement for needed signatures

Incorporate into air engagement SSP Finalize

User validation/review Assess signature support


Estimation Environment

MeasuredSignature Data

Holdings

NSP Distributed Signature Data

Centers

Modeled Signature Holdings

NSP Customer

Communities

NSP Distributed Signature Modeling

Centers

Modeling & simulation (M&S) tools to estimate signatures for environments and collectors not specifically available in NSP measured signature holdings


Visualization Tool

Signatures data relating to any collections asset can beloaded into the signatures visualization tool

GapsPlatformSensorLocationCoveragePhenomenology


Gap IdentificationOptimized SignaturesGaps

Current Collection

Architecture

Temporal

Spatia

l

Spectral

Energy

Lower weightedRelevant Background


Summary

Target and background signature understanding is vital to achieve the promise of spectral technologies

NSP is a one-stop federated signatures and signature data source

To be versatile, signature data must be Measured Integrated Accessible by analysts and developers

NSP is ready to accept, integrate, and provide relevant signature data

promise of spectral and signatures understanding

Documents

product streams

hurricane damage

key players

capabilities of single

radio frequency

infrared spectrometry