multispectral and hyperspectral scanning

Multispectral and Hyper spectral remote sensing and its applications

Agricultural College, Bapatla

Class seminar on

By

Medida Sunil Kumar

BAD-14-06

1

Introduction

• Natural color image data is comprised of red, green, and

blue bands.

• Color infrared data is comprised of infrared, red, and

green bands.

• For multispectral data containing more than 3 spectral

bands, the user must choose a subset of 3 bands to display

at any given time

Multispectral image

Multispectral imagery generally refers to 3 to 10 bands that are

represented in pixels. Each band is acquired using a remote sensing

radiometer.

Examples of MSS sensors

Multispectral Imaging using discrete detectors and scanning mirrors

Landsat Multispectral Scanner (MSS)

Landsat Thematic Mapper (TM)

NOAA Advanced Very High Resolution Radiometer (AVHRR)

NASA and ORBIMAGE, Inc., Sea-viewing Wide field-of-view Sensor

(SeaWiFS)

Daedalus, Inc., Aircraft Multispectral Scanner (AMS)

NASA Airborne Terrestrial Applications Sensor (ATLAS)

Multispectral Imaging Using Linear Arrays

SPOT 1, 2, and 3 High Resolution Visible (HRV) sensors and Spot 4 and 5 High

Resolution Visible Infrared (HRVIR) and vegetation sensor

Indian Remote Sensing System (IRS) Linear Imaging Self-scanning Sensor

(LISS)

NASA Terra Advanced Space borne Thermal Emission and

Reflection Radiometer (ASTER)

NASA Terra Multiangle Imaging Spectro Radiometer (MISR)

Imaging Spectrometry Using Linear and Area Arrays

NASA Jet Propulsion Laboratory Airborne Visible/Infrared Imaging

Spectrometer (AVIRIS)

Compact Airborne Spectrographic Imager 3 (CASI 3)

NASA Terra Moderate Resolution Imaging Spectrometer (MODIS)

NASA Earth Observer (EO-1) Advanced Land Imager (ALI),

Hyperion, and LEISA Atmospheric Corrector (LAC)

4 m multi-spectral

1 m panchromatic

IKONOS Imagery Sample: Bound Brook NJ

Sketch of a multispectral system providing an open

system interface

Spectral bandsBlue: 450-515..520 nm, is used for atmosphere and deep water imaging, and can reach

depths up to 150 feet (50 m) in clear water.

Green: 515..520-590..600 nm, is used for imaging vegetation and deep water

structures, up to 90 feet (30 m) in clear water.

Red: 600..630-680..690 nm, is used for imaging man-made objects, in water up to 30

feet (9 m) deep, soil, and vegetation.

Near infrared: 750-900 nm, is used primarily for imaging vegetation.

Mid-infrared: 1550-1750 nm, is used for imaging vegetation, soil moisture content,

and some forest fires.

Mid-infrared: 2080-2350 nm, is used for imaging soil, moisture, geological features,

silicates, clays, and fires.

Thermal infrared:10400-12500 nm, uses emitted instead of reflected radiation to

image geological structures, thermal differences in water currents, and fires, and for

night studies.

Radar: Radar and related technologies are useful for mapping terrain and for detecting

various objects.

Spectral band usage True-color:

Uses only red, green, and blue channels, mapped to their

respective colors. As a plain color photograph, it is good for analyzing

man-made objects, and is easy to understand for beginner analysts.

Green-red-infrared:

Where the blue channel is replaced with near infrared, is used for

vegetation, which is highly reflective in near IR; it then shows as

blue. This combination is often used to detect vegetation and

camouflage.

Blue-NIR-MIR:

Where the blue channel uses visible blue, green uses NIR (so

vegetation stays green), and MIR is shown as red. Such images allow

the water depth, vegetation coverage, soil moisture content, and the

presence of fires to be seen, all in a single image.

Multispectral Scanning

• A scanning system used to collect data over a variety of different

wavelength ranges is called a multispectral scanning (MSS)

• Scanning systems can be used on both aircraft and satellite

platforms

• There are two main modes or methods of scanning employed to

acquire multispectral image data

1. Across-track scanning (Whisk broom scanners)

2. Along-track scanning (Push broom scanners)

1. Across-track scanning Scan the Earth in a series of lines

Lines perpendicular to sensor motion

Each line is scanned from one side of the sensor

to the other, using a rotating mirror (A).

Internal detectors (B) detect & measure energy for

each spectral band, convert to digital data

IFOV or Instantaneous Field of View (C) of the

sensor and the altitude of the platform determine the

ground resolution cell viewed (D), and thus the

spatial resolution.

The angular field of view (E) is the sweep of the

mirror, measured in degrees, used to record a scan

line, and determines the width of the imaged swath

(F).

http://ccrs.nrcan.gc.ca/resource/tutor/fundam/chapter2/08_e.php

Characteristics of Daedalus Airborne Multispectral across tract

Scanner

2. Along-track scanning

Uses forward motion to record successive scan lines

perpendicular to the flight direction

Linear array of detectors (A) used; located at the focal

plane of the image (B) formed by lens systems (C)

Separate array for each spectral band

Each individual detector measures the energy for

a single ground resolution cell (D)

May be several thousand detectors

Each is a CCD

Energy detected and converted to digital data

“Pushed" along in the flight track direction (i.e. along

track).

http://ccrs.nrcan.gc.ca/resource/tutor/fundam/chapter2/08_e.php

Push broom scanner video

CCD arrays

Passive system

Line or block of CCDs instead of scanning

mirror

Senses entire swath (or block) simultaneously

CCD

Advantages of whisk broom scanners

Larger IFOV:

Greater quantity of total energy on a detector

More sensitive scene radiance measurements due to

higher signal levels

Improved radiometric resolution

Signal greater than back ground noise

Higher signal-to – noise ratio

Longer dwell time

Advantages of Along-track scanners

Measure the energy from each ground resolution cell for a

longer period of time

More energy to be detected and improves the radiometric

resolution

Smaller IFOVs and narrower bandwidths for each detector

Cross-calibrating thousands of detectors to achieve uniform

sensitivity across the array is necessary and complicat

Advantages of Push broom over Whisk broom

Longer dwell time stronger signal, greater range

of sensed signal better spatial and radiometric

resolution

Better geometry (fixed relationship among

detect or elements)

Lighter and smaller devices, require less energy

Disadvantages

• Need t o calibrate more detectors

• Limited range of spectral sensitivity of commercially

available CCDs

Difference between across and along tract scanning

Particulars Object plane

scanning

Image plane

scanning

Scanning mechanism Mirror for

rotating/tilting

No mechanism

Width of scanning Wide Narrow

Field of view (IFOV) Narrow Wide

Aperture optics Large Small

Optical system Catoptic system Dio/catoptic system

Observation range Visible ----thermal Visible ---- Near

thermal

Number of optical

detector

Few Many (Area array)

Signal noise ration Low High

Size & weight Large & heavy Small & light

LANDSAT MSS SystemPush broom

Landsat-8 produces 11 images with the followingbands:

Band 1: Coastal aerosol (0.43-0.45 um)

Band 2: Blue (0.45-0.51 um)

Band 3: Green (0.53-0.59 um)

Band 4: Red (0.64-0.67 um)

Band 5: Near infrared NIR (0.85-0.88 um)

Band 6: Short-wave Infrared SWIR 1 (1.57-1.65 um)

Band 7: Short-wave Infrared SWIR 2 (2.11-2.29 um)

Band 8: Panchromatic (0.50-0.68 um)

Band 9: Cirrus (1.36-1.38 um)

Band 10: Thermal Infrared TIRS 1 (10.60-11.19 um)

Band 11: Thermal Infrared TIRS 2 (11.50-12.51 um)

SPOT sensors have the capability to scan 27 degrees off-nadir, allowing for repeat coverage of an area every two to twenty-six days.

Steerable mirror

LISS-III on Resourcesat-2

Research and Commercial Imaging Spectrometers

(Hyperspectral)

CategoryBest

BandsSalient Characteristics

Clear Water 7 Black tone in black and white and color.

Silty Water 4,7 Dark in 7; bluish in color.

Nonforested Coastal

Wetlands7

Dark gray tone between black water and light gray land; blocky pinks, reds, blues,

blacks.

Deciduous Forests 5,7 Very dark tone in 5, light in 7; dark red.

Coniferous Forest 5,7Mottled medium to dark gray in 7, very dark in 5; brownish-red and subdued tone in

color,

Defoliated Forest 5,7Lighter tone in 5, darker in 7 and grayish to brownish-red in color, relative to normal

vegetation.

Mixed Forest 4,7 Combination of blotchy gray tones; mottled pinks, reds, and brownish-red.

Grasslands (in growth) 5,7 Light tone in black and white; pinkish-red.

Croplands and Pasture 5,7Medium gray in 5, light in 7, pinkish to moderate red in color depending on growth

stage.

Moist Ground 7 Irregular darker gray tones (broad);darker colors.

Soils-bare Rock-Fallow

Fields4,5,7

Depends on surface composition and extent of vegetative cover. If barren or exposed,

may be brighter in 4 and 5 than in 7, Red soils and red rock in shades of yellow; gray

soil and rock dark bluish; rock outcrops associated with large land forms and

structure.

Faults and Fractures 5,7Linear (straight to curved), often discontinuous; interrupts topography; sometimes

vegetated.

Sand and Beaches 4,5 Bright in all bands; white, bluish, to light buff.

Stripped Land-Pits and

Quarries4,5

Similar to beaches – usually not near large water bodies; often mottled, depending on

reclamation.

Urban Areas:

Commercial Industrial5,7

Usually light toned in 5, dark in 7, mottled bluish-gray with whitish and reddish

specks.

Urban Areas: Residential 5,7 Mottled gray, with street patterns visible; pinkish to reddish.

Transportation 5,7 Linear patterns, dirt and concrete roads light, in 5; asphalt dark in 7.

Best MSS Bands for Identifying Surface Features

Thematic Mapper (TM) A more sophistical multispectral imaging sensor, named the Thematic Mapper (TM) has

been added to Landsat-4 (1982) onwards.

Six reflectance bands obtain their effective resolution at a nominal orbital altitude of 705

km (438 miles) through an IFOV of 0.043 m rad. The seventh band is the thermal channel,

which has an IFOV of 0.172 m rad.

LANDSAT Thematic Mapper

Landsat TM-7 bands-8 bit data

Spectral(where we look)

Radiometric(how finely can wemeasure the return)0-63, 0-255, 0-1023

Landsat TM BAND 1 2 3 4 5 7 6

Hyperspectral image Multispectral – Many spectra (bands)

Hyperspectral – Huge numbers of continuous bands

Hyperspectral remote sensing provides a continuous, essentially complete

record of spectral responses of materials over the wavelengths considered.

Hyperspectral imagery consists of much narrower bands (10-20 nm). A

hyperspectral image could have hundreds of thousands of bands.

This uses an imaging spectrometer.

Higher level of spectral detail in hyperspectral images gives better

capability to see the unseen.

Adds a level of complexity

Finer spectral resolution or wider spectral coverage

Current and Recent Hyperspectral Sensors & data providersJadhav, 2014

International Journal of Computer Applications. 106 (7); 38-42

Altitude and area coverage regimes for HYDICE,

AVIRIS, and Hyperion (on EO-1) hyperspectral

sensor platforms

Swath of Landsat-7 multispectral sensor (185 km), experimental hyperspectral sensors

HYDICE (0.25 km), AVIRIS (11 km), and Hyperion (7.6 km).

Comparison of Hyperspectral Imaging Systems

HYDICE= Hyperspectral Digital Imagery Collection Experiment

AVIRIS= Advanced Visible/Infrared Imaging Spectrometer

Hyperion= VNIR/SWIR hyperspectral sensor

Lincoln Laboratory Journal. 14; 1-28

Shaw and Burke, 2003

Differences between multispectral and hyper spectral scanning

Parameters Hyperspectral Imaging Multispectral Imaging

Images per dataset More than 20 2-20

Spectral Info Full spectrum per pixel 4 to 20 data points per pixel

Spectral width per image 10 nm per image 30 nm per image

Processing Methods spectral and image Limited image

Result Highest discrimination Moderate discrimination

Applications

Spectral Signatures of 4 Materials

Band 1 = 0.55 um Band 2 = 0.85 um

Mineral exploration

www.remotesensing-geology.ic.cz

Veronika et al.,2013

Mineral mapping

www.remotesensing-geology.ic.cz

Veronika et al.,2013

Reflectance spectra of some representative minerals

http://www.microimages.com

The continuum removed spectra are fit together using a modified least squares calculation. Kaolinite is the

best match to the Cuprite spectrum. The muscovite spectrum has two features, one near 2.2 and the other near

2.3 µm. No 2.3-µm muscovite feature could be detected in the Cuprite spectrum, so the weighted fit is zero

(left hand column). Note the very similar fits between kaolinite (0.996) and halloysite (0.963), yet the

halloysite profile clearly does not match as well as the kaolinite profile. This illustrates that small differences

in fit numbers are significant. Alunite has two diagnostic spectral features, but the 1.5-µm feature is not

shown.

Reflectance spectra of some representative materials

by ASTER Spectral Library

http://www.microimages.com

Source: http://popo.jpl.nasa.gov/html/data.html

Spectral absorption features due to different

organic compounds in different grades of coalRamakrishnan and Rishikesh Bharti, 2015

Current Science. 108(5); 879-891

Spectral absorption positions for various minerals

(Ramakrishnan & Rishikesh

Bharti, 2015)

Current Science. 108(5); 879-891

Advanced Very High Resolution Radiometer

(AVHRR) MSS Image of Sea Surface Temperatures

www.deepseawaters.com

http://www.deepseawaters.com/avhrr.htm

Radiative transfer of optical underwater scenarios

for characterizing phytoplankton's distribution

through the water column. dzyr rsezy TYWTWETEf

Spectral absorption & reflectance's dominant phytoplankton

groupsTorrecilla and Piera, 2009

http://www.intechopen.com/books/advances-in-geoscience-and-remotesensing/derivative-analysis-of-hyperspectral-oceanographic-data

Ocean colour monitor by SeaWiFS Atlantic Ocean

Decreased Blue and increased green reflectance due to chlorophyll

Gary and Burke, 2003

Lincoln Laboratory Journal. 14 (1); 3-28

MODIS MSS image of suspended particulate matter

in water bodies Gary and Burke, 2003

Lincoln Laboratory Journal. 14 (1); 3-28

Zonal mean record of total ozone obtained from global ozone monitoring

experiment (GOME), scanning imaging absorption spectrometer for atmospheric

chartography (SCIAMACHY) and GOME-2 data from April 1995 to December

2007

Monthly mean total ozone for the month

September

Diego Loyola et al., 2009

Twenty Years of Ozone Decline, C. Zerefos et al. (eds.),

Published by Springer Science+Business Media B.V. 2009,

pp-213-239

Spectral signatures of pure sand and oil-impacted

sand Andreoli et al., 2007)

http://www.jrc.cec.eu.int.http://www.jrc.cec.eu.int.http://www.jrc.cec.eu.int.

http://www.jrc.cec.eu.int.

Identification of oil spills

Typical reflectance values for snow (blue), bare soil

(black), forest canopy (pink), and cirrus (red) and stratus

(green) clouds as a function of wavelength (micrometers).Gary Jedlovec, 2009

http://www.intechopen.com/books/advances-in-geoscience-and-remote-sensing/automated-detection-of-clouds-in-satellite-imagery

MODIS identifies cloud classes

Hi cld

Mid cld

Lo cld

Snow

clr

8.6 um

11 um

Water

Ice

Clouds separate into classes using MODIS MSS data

Hi cld

Snow

Lo cld

Clear

Mid cld

11 um 11 um

vis

LSD 1.6 um

1.6 um

8.6-11 um

low clouds =4-11 μmHigh clouds and snow reflectance =0.65 μm

Global aerosol pattern for July derived from three satellite sensors

MISR=Multi-angle Imaging SpectroRadiometer

POLDER=POLarization and Directionality of the Earth Reflectances sensor

TOMS=Total Ozone Mapping Spectrometer

Husar, 2004

http://capita.wustl.edu/CAPITA/CapitaReports/031001IntercontinentalDustTransport/IntercontDustTransport.htm

Ship Tracks occur in marine

stratocumulus regions (California,

Azores, Namibia, and Peru)

Conditions for formation

(Associated features)

High humidity

Small air-sea temperature

difference

Low wind speed

Boundary layer between 300

and 750 m deep

Enhanced reflectance of clouds

at 3.7 µm

Larger number of small droplets

arising from particulate

emission from ships

MODIS detects ship tracks

http://www.google.co.in/imgres?imgurl=http://earthobservatory.nasa.gov/Features/Aerosols/images/pacific_tmo_2009063_crop.jpg&imgrefurl=ht

Particles emitted by ships increase concentration of

Cloud condensation nuclei (CCN) in the air

Increased CCN increase concentration of cloud droplets and reduce average size of

the droplets

Increased concentration and smaller particles reduce production of drizzle (100 µm

radius) droplets in clouds

Liquid water content increases because loss of drizzle particles is suppressed

Clouds are optically thicker and brighter along ship track

AVIRIS sensor red-green-blue (RGB) imagery of foothills near Linden,

California. Left image is a pseudo-natural color depiction, Right image is a

false-color details visible at shortwave-infrared band signatures(Michael and Burke, 2003).

Lincoln Laboratory Journal. 14 (1); 29-54.

Spectral curves for nine features identified in the AVIRIS sensor

imagery of above imageMichael and Burke, 2003

Lincoln Laboratory Journal. 14 (1); 29-54.

Ash plume detection during Mt. Cleveland eruption (19 Feb 2001)

Investigating with Multi-spectral

Combinations

Given the spectral response of a surface or

atmospheric feature

Select a part of the spectrum where the

reflectance or absorption changes with

wavelength

e.g. transmission through ash

If 11 μm sees the same or higher BT than 12 μmthe atmosphere viewed does not containvolcanic ash;if 12 μm sees considerably higher BT than 11μm then the atmosphere probably containsvolcanic ash

trans

Wave Length

11 μm

12 μm

Volcanic Ash

3

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

7 8 9 10 11 12 13 14

wavelength (m)

transmission (total)transmission (scattering)transmission (absorption)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 8 9 10 11 12 13 14

wavelength (m)


4 50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 8 9 10 11 12 13 14

tran

smis

sion

9

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 8 9 10 11 12 13 14

tran

smis

sion

9

4 5

T10.8 - T12.0 > 0 water & ice

T10.8 - T12.0 < 0 volcanic ash

Source: Dr. M. Watson, Michigan Technical University

Ice Ash

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 8 9 10 11 12 13 14


Wavelength (m) Wavelength (m)

Spectral features of ice and ash in

the 10-13 m waveband

AVHRR channels AVHRR channels

ET-ODRRGOS, Oxford, UK, 1-5 July

2002

absorption

scattering

total

9 10 11 12 13 14wavelength

3

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

7 8 9 10 11 12 13 14

wavelength (m)


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 8 9 10 11 12 13 14

wavelength (m)


4 50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 8 9 10 11 12 13 14

tran

smis

sion

9

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 8 9 10 11 12 13 14

tran

smis

sion

9

4 5

T10.8 - T12.0 > 0 water & ice

T10.8 - T12.0 < 0 volcanic ash

Source: Dr. M. Watson, Michigan Technical University

Ice Ash

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 8 9 10 11 12 13 14


Wavelength (m) Wavelength (m)

Spectral features of ice and ash in

the 10-13 m waveband

AVHRR channels AVHRR channels

ET-ODRRGOS, Oxford, UK, 1-5 July

2002

BT11-BT12 > 0 for ice

BT11-BT12 < 0 for volcanic ash

Frank Honey 1980s

Huete et al, 2001

EVI 2.5NIR red

1 NIR 6 red 7.5 blue

Measuring grain fall intensity

Classification of Disease index images derived

from PHI airborne images Huang et al., 2012

http://www.intechopen.com/books/remote-sensing-applications/crop-disease-and-pest-monitoring-by-remotesensing

Spectral reflectance of winter wheat leaf un-infested

and infested by aphid Huang et al., 2012


Spectral reflectance of healthy and infested wheat

crop by various aphid (Sitobion avenae) damage levelsHuang et al., 2012


Spectral reflectance of winter wheat leaf un-infested and

infested by leaf yellow rust (Biotroph Puccinia striiformis)Huang et al., 2012


Comparisons between hyperspectral reflectance of cotton

Honeydew produced by whiteflies (Bemesia tabaci ),

A leaf covered with a secondary mold Aspergillus sp. Growing on the whitefly honeydew,

Chlorotic leaf without honeydew

Huang et al., 2012


Mean Yield (t/ha) (± 95% confidence) Crop Water Stress Index values (± 95%

confidence)

Treatment 2004 2005 Treatment Oct 2004 Oct 2005

Irrigated, 0 kg/ha N 4.16 ± 0.20 3.44 ± 0.25 Irrigated, 0 kg/ha N 0.38 ± 0.05 0.09 ± 0.03

Irrigated, 17 kg/ha

N

4.62 ± 0.22 - Irrigated, 17 kg/ha N 0.36 ± 0.10 -

Irrigated, 39 kg/ha

N

4.58 ± 0.20 3.48 ± 0.19 Irrigated, 39 kg/ha N 0.31 ± 0.07 0.07 ± 0.04

Irrigated, 163 kg/ha

N

3.87 ± 0.41 - Irrigated, 163 kg/ha

N

0.21 ± 0.04 -

Rainfed, 0 kg/ha N 1.48 ± 0.55 2.84 ± 0.17 Rainfed, 0 kg/ha N 0.81 ± 0.07 0.30 ± 0.02

Rainfed, 17 kg/ha N 1.39 ± 0.66 - Rainfed, 17 kg/ha N 0.82 ± 0.04 -

Rainfed, 39 kg/ha N 1.44 ± 1.03 3.00 ± 0.10 Rainfed, 39 kg/ha N 0.72 ± 0.08 0.30 ± 0.01

Rainfed, 163 kg/ha

N

0.52 ± 0.30 - Rainfed, 163 kg/ha

N

0.75 ± 0.04

Effect of irrigation and different levels of nitrogen on

Crop- water stress index of wheat crop Leary et al., 2006

http://www.regional.org.au/au/asa/2006/plenary/technology/4584_tillingak.htm

multispectral and hyperspectral scanning

Education