Hyperspectral Simulation of an Arctic Landscape with ISDASv2 · 2015. 9. 29. · e_em19 e_em20...

Hyperspectral Simulation of an Arctic Landscape with ISDASv2

H. Peter White, Lixin Sun, Matthew Maloley

Canada Centre for Remote Sensing, Natural Resources Canada [email protected]

Canada Centre for Remote Sensing Imaging Spectrometry Science Team

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Content

Quick introduction/motivation into hyperspectral remote sensing

Hyperspectral sensors and processing system

Hyperspectral availability

Preparation and processing

Simulation capacity and application

Some examples

Final comments


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Motivation

Many mineralogical areas of economic interest are located in the

arctic regions of Canada.

Canada’s arctic contains sensitive habitats that are impacted by

climate change and by the exploitation of natural resources.

Region is remote, with often with little or inadequate access.

Limited snow-free periods, limited solar illumination periods.

Many rock-forming minerals and biogenic covers exhibit unique

spectral characteristics that can be exploited.

Advance hyperspectral techniques to support exploration of these

regions.


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Hyperspectral Remote Sensing Images are acquired simultaneously in many contiguous spectral bands, producing a spectral ‘signature’ for each pixel.

Now exploit the magnitude and shape characteristics of spectral features

Biochemical Geochemical Structural.


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Spectral Signatures of Different Target Types


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Spectral Signatures of Different Target Types

Sentinel-2

bands


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Hyperspectral Missions – At a Glance SENSOR EO-1

Hyperion (U.S.A.)

Proba-1 CHRIS

(ESA)

HICO

(USA)

PRISMA

(Italy)

EnMAP

(Germany)

HISUI (Japan)

HyspIRI

(U.S.A)

WaterSAT

(Canada Proposed)

CHM Constellation

(Canada Proposed)

SHALOM

(ITA/ISR)

Proposed Launch

2000

Launched

2001

Launched

2009 Launched

≥2016 ≥2017

≥2019 ≥2020 ≥2020 ≥2020 ≥2020

Bandwidth Coverage

VNIR + SWIR

VNIR VNIR VNIR + SWIR

VNIR + SWIR

VNIR + SWIR

VNIR + SWIR +

~4–12 μm T

VNIR VNIR (+ SWIR Band?)

VNIR + SWIR

Spatial Resolution

30 m 17 m 90 m 20 – 30 m

30 m

15 – 30 m 60 m H;

45m T

~ 100 m ≤30 m ???

Spectral Resolution

~ 11 nm ~ 10 nm ~ 6 nm 10 nm 6.5 - 10 nm

10 – 12 nm

10 nm H; .08 - .54

μm T

~ 5-10 nm ~ 5-10 nm ???

Swath 7.5 km 560 km 42 km 30 – 60 km

30 km 30 km H 90 km M

~145 km H

400 - 600 km T

~ 250 km ~ 250 km X 3

???

Baseline Values – To be revisited to match technological innovation with user applications

WaterSAT : http://www.asc-csa.gc.ca/eng/media/backgrounders/2014/0429.asp CHM Constellation : http://www.mdacorporation.com/corporate/news/pr/pr2014042301.cfm


Evaluate and prepare for regular multi-sensor space-borne hyperspectral acquisitions of Canada’s arctic. To handle both spectral and spatial information. To handle large data volumes in a timely fashion What can we exploit from this data source to advance our capacity to

interpret and disseminate information?

Simulate a satellite based sensor – ISDASv2 Create a “super-cube” Simulate space borne recorded hyperspectral at-sensor radiance

(including all sensor artefacts) Independent pre-process to at-surface reflectance

(can we independently detect/rectify sensor influences on the data)

Preparation

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General Processing Scheme – ISDASv2

Observed Hyperspectral Data Cube

Assess and Calibrate for Sensor/Calibration/Platform Artefacts

(Noise, Smile, Keystone, Offsets)

Remove Atmospheric Effects

Extract Information

Information Product

Spectral Library

Data Preprocessing

Data Input

Information Extraction

Output

All arrows are in both directions.


ISDASv2 - Data Visualization 10 / 16


Endmembers (field data)

0

20

40

60

80

400 900 1400 1900 2400

Wavelength (nm)

Nad

ir R

efl

ecta

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e_em1e_em2e_em3e_em4e_em5e_em6e_em7e_em8e_em9e_em10e_em11e_em12e_em13e_em14e_em15e_em16e_em17e_em18e_em19e_em20

Strathcona Sound Formation

Victoria Bay Formation

Simulation 1 : Nanisivik, Nunavut 11 / 16

Society Cliffs Formation


Landsat RGB image Simulated hyperspectral RGB image

Simulation 1 : Nanisivik, Nunavut 12 / 16


Water/Shadow

Snow

Vegetation

Lichen

Limestone

Dolostone

Pink Rock

Dark Rock

Simulated end-members defined using airborne (Probe-1)

and space borne (Landsat) imagery, End-member

spectra from field campaign (GER3700).

• Scene Simulator • Create at-surface reflectance cube

• Derive TOA (at-sensor) radiance

• Convolve to sensor spatial/spectral

resolution and sensor characteristics

• Independently derive at-surface

spectral reflectance

• Indept. determine end-members

• Reveal efficacy of space borne hyperspectral

for surficial mineralogical mapping.

• Hyperspectral simulations, as employed

in the ISDASv2 Software Package,

supports simulation and application

cal/val for other new sensors, such

as EnMAP and Sentinel.

Derived end-members from atmospherically corrected

simulated space borne hyperspectral imagery.

The Nanisivik Mine Site is located in the Borden Rift Basin of northern Baffin Island (N73°/W84.5°) which is located in a High Arctic ecozone, with sparse tundra vegetation

dominated by lichens, mosses, and grasses. The local geology consists of Late Proterozic sedimentary rocks which have been deposited within a

deepening rift basin. This regions hosts an economic concentration of Zn-Pg mineralization. Hyperspectral technology can capture the unique spectral

features of many alteration and rock-forming minerals supporting exploration. Additional studies are now proposed in the Arctic region via the GEM-II

Program, including the Hackett River and Hope Bay Greenstone Belts (Nunavut), host to several volcanogenic massive sulphide deposits. Hydrothermal

alteration styles (e.g., chloritization, sericitization, carbonatization) associated with volcanogenic massive sulfides (VMS) and orogenic gold deposits are

mineralogically ideal for detection by hyperspectral methods.

Image simulations (derived with the in-house developed ISDASv2 Software Package) are advancing our understanding of how to exploit airborne and space borne

hyperspectral remote sensing to support geological exploration and environmental monitoring.

Nanisivik, Nunavut 13 / 16


Izok Lake, Nunavut 14 / 35

IZOK LAKE

Simulated Landsat bands

Simulated Sentinel-2 bands

Simulated EnMAP bands


Hackett River, Nunavut 15 / 35

Hackett River

2009 Hackett River, NU.: Exploiting airborne hyperspectral imaging to develop capacity to detect and map alteration mineralogical spectral characteristics, targeting regions for exploration of base and precious metals (Cu, Zn, Ag, Pb).

Arctic Mineral Exploration

Working with OGD and mineral exploration companies to remotely target volcanic hosted massive sulphide (VHMS) deposits and alteration zones to focus regional exploration of mineral deposits in Canada’s Arctic.


Sensor Simulation – Evaluating Modelling imagery

Support multi-sensor temporal coverage.

Advance application development (be prepared!)

Demonstrate strengths, where, when and how does space borne hyperspectral remote sensing contribute to Northern exploration.

Generate scenes through an end-to-end image cube simulation that includes: modelling sensor characteristics

atmospheric influences

surface component mixtures.

This is followed by an independent evaluation of information extraction methods. The image analysis needs to have no knowledge

of the simulation characteristics beyond what a data provider would supply.

Can now provide quantitative analysis of efficacy of an application relative to sensor (multi or hyper).

Endmembers (field data)

0

20

40

60

80

400 900 1400 1900 2400

Wavelength (nm)

Nad

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efl

ecta

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e_em1e_em2e_em3e_em4e_em5e_em6e_em7e_em8e_em9e_em10e_em11e_em12e_em13e_em14e_em15e_em16e_em17e_em18e_em19e_em20

Application (How do

the spectra mix?)

Orig. Data

Simulation

Exploration

Spectral Analysis

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Atm + Sensor

Characteristics


Thank you for your attention.

R. Neville, L. Sun and K. Staenz “Spectral calibration of imaging spectrometers by atmospheric absorption feature matching”, Can. J. Rem. Sens. 34:S29-S42, 2008.

K. Staenz, T. Szeredi, and J. Schwarz “ISDAS- A system for processing/analyzing hyperspectral data.”, Can. J. Rem. Sens. 42(2):99-113, 1998.

H.P. White, S.K. Khurshid, R. Hitchcock, R. Neville, L. Sun, C.M. Champagne and K. Staenz, “From at-sensor observation to at-surface reflectance – calibration steps for earth observation hyperspectral sensors”, IEEE IGARSS2004, Anchorage, U.S., 2004

A. Berk, L. S. Bernstein, and D. C. Robertson, “MODTRAN: A moderate resolution model for LOWTRANS7”, final report, GL-TR-0122, AFGL, Hanscom AFB, Maryland, U.S.A., 1989.

R.J. Soffer, R.A. Neville, K. Staenz and H.P. White, “Impact of Spectrally Dependent Gain Errors in Hyperspectral Data on the Determination of Chlorophyll Concentrations in Vegetation”, IEEE IGARSS2007, Denver, U.S., 2007.

L. Sun, Y. Zhang and B. Guindon, “Improved iterative error analysis for endmember extraction from hyperspectral imagery”, Proceedings of SPIE, 10-14 August. San Diago. USA., 2008.

H.P. White, L. Sun and R. Gauthier, “Independent evaluation of EnMAP sensor for geological mapping in arctic Canada”, in Proc 2nd Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing, 2010

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Hyperspectral Simulation of an Arctic Landscape with ISDASv2 · 2015. 9. 29. · e_em19 e_em20...

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