characteristics of sensor systems - puc rio

Characteristics of Sensor SystemsMultispectral, Hyperspectral, Thermal Sensing

Raul Q. FeitosaGilson. A. O. P. Costa

Patrick N. Happ

Contents

Introduction

Spectral Resolution

Characteristics of Orbital Platforms

Sensor Characteristics

Spatial Resolution

Pan-sharpening

Radiometric Resolution

Temporal Resolution

2

Remote Sensing SystemsPassive (Optical) Systems

Do not have their own source of radiation, so they depend on solar radiation as the energy to illuminate/capture the scene.

They measure the reflected solar radiation and/or the radiation emitted by the targets.

Remote Sensing SystemsActive Systems

Have their own source of electromagnetic radiation, usually working in narrow bands of the electromagnetic spectrum (microwave).

Remote Sensing Images

Remote Sensing Systems

CCD Sensor

WorldView 2

• Systems deliver digital numbers (DN)

• Images: picture element (pixel)

• Signal (radiance) integrated

• Signal (radiance) discretized CPan

BG

RY

RENIR1

NIR2

Digital Sensors

Band 1 (Visible Blue) Landsat 7 TM

0.45–0.52µm

Band 2 (Visible Green) Landsat 7 TM

0.52–0.60µm

Band 3 (Visible Red) Landsat 7 TM

0.63–0.69µm

Band 4 (Near IR) Landsat 7 TM

0.76–0.90µm

Band 5 (Shortwave IR) Landsat 7 TM

1.55–1.75µm

Band 7 (Shortwave IR) Landsat 7 TM

2.08–2.35µm

Band 6 (Thermal IR) Landsat 7 TM

10.4–12.4µm

Spectral ResolutionSpectralBands

Sentinel-2

Spectral Resolution

Blu

e B

an

d

Red

Ban

d

Gre

en

Ban

d

NIR

Ban

d

IKONOS

Co

lor

Co

mp

osit

e

R (

Red

Ban

d)

-G

(G

reen

Ban

d)

-B

(B

lue B

an

d)

IKONOST

rue C

olo

r

Spectral Resolution

Co

lor

Co

mp

osit

e

R (

NIR

Ban

d)

-G

(R

ed

Ban

d)

-B

(G

reen

Ban

d)

Fals

e C

olo

rIKONOS

Spectral Resolution

IKONOS

Spectral Resolution

• The main difference between multispectral and hyperspectral is the number of bands and how narrow the bands are.

• Multispectral imagery generally refers to 3 to 10 bands.

• Hyperspectral imagery consists of much narrower bands (10-20 nm). A hyperspectral image could have hundreds or thousands of bands.

Spectral Resolution

8 bandas 24 bandas 72 bandas

Hyperspectral sensors

Spectral Resolution

MODIS (Hyperspectral)

Band number Range Primary use1 0.620 - 0.670 Cover Transformation, Vegetation Chlorophyll2 0.841- 0.876 Cloud Amount, Cover Transformation3 0.459 – 0.479 Soil/Vegetation Differences4 0.545 - 0.565 Green Vegetation5 1.230 - 1.250 Leaf/Canopy Differences6 1.628 – 1.652 Snow/Cloud Differences7 2.105-2.155 Cloud Properties, Land Properties8 0.405 - 0.420 Chlorophyll9 0.438 - 0.448 Chlorophyll

10 0.483 - 0.493 Chlorophyll11 0.526 - 0.536 Chlorophyll12 0.546 - 0.556 Sediments

13h 0.662 - 0.672 Atmosphere, Sediments13l 0.662 - 0.672 Atmosphere, Sediments14h 0.673 - 0.683 Chlorophyll Fluorescence14l 0.673 - 0.683 Chlorophyll Fluorescence15 0.743 - 0.753 Aerosol Properties16 0.862 - 0.877 Aerosol Properties, Atmospheric Properties17 0.890 - 920 Atmospheric Properties, Cloud Properties18 0.931 - 0.941 Atmospheric Properties, Cloud Properties19 0.915 - 0.965 Atmospheric Properties, Cloud Properties20 3.660 - 3.840 Sea Surface Temperature21 3.929 - 3.989 Fires & Volcanoes22 3.929 - 3.989 Cloud Temperature, Surface Temperature23 4.020 - 4.080 Cloud Temperature, Surface Temperature24 4.433 - 4.498 Cloud Fraction, Troposphere Temperature25 4.482 - 4.549 Cloud Fraction, Troposphere Temperature26 1.360 - 1.390 Cloud Fraction (Thin Cirrus), Troposphere Temperature27 6.535 - 6.895 Mid Troposphere Humidity28 7.175 - 7.475 Upper Troposphere Humidity29 8.400 - 8.700 Surface Temperature30 9.580 - 9.880 Total Ozone31 10.780 - 11.280 Cloud Temperature, Fires, Volcanoes, Surface Temperature32 11.770 - 12.270 Cloud Height, Fires & Volcanoes, Surface Temperature33 13.185 - 13.485 Cloud Fraction, Cloud Height34 13.485 - 13.785 Cloud Fraction, Cloud Height35 13.785 - 14.085 Cloud Fraction, Cloud Height36 14.085 - 14.385 Cloud Fraction, Cloud Height

• Bands 1, 2: 250m

• Bands 3-7: 500m

• Bands 8-36: 1km

Spectral Resolution

Hyperion (Hyperspectral)

https://eo1.usgs.gov/sensors/hyperioncoverage

Spectral Resolution

• Provides a high resolution hyperspectral imager capable of resolving 220 spectral bands (from 0.4 to 2.5 µm) with a 30-meter resolution.

• The instrument can image a 7.5 km by 100 km land area per image, and provide detailed spectral mapping across all 220 channels with high radiometric accuracy.

https://eo1.usgs.gov/sensors/hyperioncoverage

• The path followed by a satellite is referred to as its orbit.

• Satellite orbits are matched to the capability and objective of the sensor(s) they carry.

• Orbit selection can vary in terms ofaltitude, orientation and rotation relative to the Earth.

Orbital Platforms (Satellites)

• Satellites at very high altitudes (~ 36.000 km), which view the same portion of the Earth's surface at all times have geostationary orbits.

• Geostationary satellites revolve at speeds that match the rotation of the Earth

• Weather and communications satellites commonly have these orbits.


GO

ES-13

(West) 7

5o

Geosta

tionary

Opera

tional E

nvironm

enta

l S

ate

llite

(G

OE

S)

Geosta

tionary

Opera

tional E

nvironm

enta

l S

ate

llite

(G

OE

S) G

OES-1

5 (W

est) 13

5o

• Satellites at very high altitudes (~ 36.000 km), which view the same portion of the Earth's surface at all times have geostationary orbits.

• Geostationary satellites revolve at speeds that match the rotation of the Earth

• Weather and communications satellites commonly have these orbits.

www.nasa.gov/mission_pages/hurricanes/archives/2012/h2012_Isaac.html


http://www.nasa.gov/mission_pages/hurricanes/archives/2012/h2012_Isaac.html

• Low Earth orbit (LEO): Geocentric orbits with altitudes up to 2000 km

• Medium Earth orbit (MEO): Geocentric orbits ranging in altitude from 2000 km to just below geosynchronous orbit at 35786 km

• Geostationary orbit (GEO): The orbit around Earth matching Earth's sidereal rotation period


• A sun-synchronous (heliosynchronous) orbit is a geocentric orbit (LEO) which passes by any given point of the Earth's surface at the same local mean solar time.

• A satellite in sun-synchronous orbit might ascend across the equator twelve times a day each time at arround 12:00hs


• Typical sun-synchronous orbits:

• altitude 600-800 km

• periods 96-100 min

• inclinations ~ 98°

• Sun-synchronous are polar orbits


• SENTINEL-2A and SENTINEL-2B occupy the same sun-synchronous orbit, but separated by 180o.

• The mean orbital altitude is 786 km. The orbit inclination is 98.62° and the Mean Local Solar Time (MLST) at the descending node is 10:30 (am).


• SENTINEL-2A and SENTINEL-2B occupy the same sun-synchronous orbit, but separated by 180o.

• The mean orbital altitude is 786 km. The orbit inclination is 98.62° and the Mean Local Solar Time (MLST) at the descending node is 10:30 (am).

• The MLST value is close to the local overpass time of LANDSAT and almost identical to that of SPOT-5, permitting the integration of SENTINEL-2 data with existing and historical missions, and contributing to long-term time series data collection.


view to side (off nadir)

SPO

T-5Swath and View Angle

Modern high resolution optical space sensors rotate the whole satellite for and during imaging

Control Moment Gyros

Reaction Wheels: one by axis

IKONOS

Swath and View Angle

Swath and View Angle

Sensor/detector configurations (optical)

• Frame camera

• Push broom scanner

• Wisk broom scanner

Sensor Characteristics (Optical)

• Digital frame camera: aerial photography

• Area CCD array CCD Sensor


• Across track scanning (whisk-broom): Landsat

• Linear CCD array CCD Sensor


• Along track scanning (push-broom): SPOT, QuickBird, IKONOS, WorldView, …

• Linear CCD array

CCD Sensor


Scanning mirrors:

• Landsat MSS TM ETM+, OLI, GOES, AVHRR, SeaWiFS, AMS, ATLAS

Linear Arrays:

• SPOT, IRS, IKONOS, CBERS, Quickbird, WorldView, ASTER, MISR

Liner and area arrays:

• AVIRIS, CASI, MODIS, ALI, Hyperion, LAC


IKONOS CCD lines

Panchromatic (2): 12 µm pitch, 13.500 pixels

Multispectral: 48 µm pitch, 3.375 pixels

IKONOS Camera

Focal length: 10m



CCD Sensor

WorldView 2





BG

RY

RENIR1

NIR2

Digital Sensors

Multiespectral

Pan

Quickbird CCD lines

IKONOS CCD lines

Multiespectral

Pan reverse

Pan forward


Quickbird

color in front

color following

IKONOS


Spatial Resolution

• Field of view (FOV)

• Instantaneous field of view (IFOV): β

• Pixel size on ground Ground Resolution Element


Spatial Resolution

d = detector size

f = focal length

β = IFOV of system

β = 2 arctan (d/2f)

tan(β/2) = (d/2) / f

β ≈ d / f

d

β

H

D

f

Spatial Resolution

Spatial Resolution: IKONOS Panchromatic

d = detector size = 12μm

f = focal length = 10m

β = ?

β ≈ 12x10-6m / 10m

β ≈ d / f

β ≈ 1.2 μrad

d

β

H

D

f

Spatial Resolution

Spatial Resolution

β = IFOV of system

H = system height

D = ground element diameter

D = 2 H tan(β/2)

tan(β/2) = (D/2) / H

D ≈ H β

d

β

H

D

f

Spatial Resolution

Spatial Resolution: IKONOS Panchromatic

β = IFOV of system = 1.2 µrad

H = system height = 681 km

D = ?

D = 681x103m x 1.2x10-6 rad

D ≈ H β

D = 0.817 m

d

β

H

D

f

Spatial Resolution

Pixel size on ground: varies with nadir/incidence angle

Spatial Resolution


Nadir angle = τ

Pixel size on ground in view direction: pv = p/cos² τ

Pixel size on ground in orbit direction: po = p/cos τ

τ = 30°, p=1m in nadir: pv= 1.33m po=1.15m

Spatial Resolution


Nadir angle = τ

Pixel size on ground in view direction: pv = p/cos² τ

Pixel size on ground in orbit direction: po = p/cos τ

Spatial Resolution

Ground sampling distance (GSD)

• Distance of neighboring pixel centers on ground – for user it looks like the pixel size on the ground

Spatial Resolution

Categorization (somewhat fuzzy)

• Low Resolution: > 100x100m (e.g., MODIS)

• Medium Resolution: > 15x15m (e.g., Landsat)

• High Resolution: > 1x1m (e.g., RapidEye)

• Very High Resolution (VHR): < 1x1m (e.g., WorldView-2)

Spatial Resolution

• Very High (Spatial) Resolution (VHR) Systems: Time-Delay Integration (TDI)

• relative speed of satellite ~ 7km/sec

• 5m in 0.7 msec: just sufficient to properly excite sensors

• 1m in 0.14 msec: too short integration time for sufficient image quality

• Integration of received energy over several pixels

Spectral/Spatial Resolution

Spatial Resolution

CCD Sensor

WorldView 2





BG

RY

RENIR1

NIR2

Digital Sensors

IKO

NO

S-2

Blu

e b

an

d (4

m)

IKO

NO

S-2

Gre

en

ban

d (4

m)

IKO

NO

S-2

Red

ban

d (4

m)

IKO

NO

S-2

NIR

ban

d (4

m)

IKO

NO

S-2

Pan

ch

rom

atic

ban

d (1

m)

IKO

NO

S-2

Co

lor C

om

po

site

RG

B (4

m)

IKO

NO

S-2

Co

lor C

om

po

site

RG

B (1

m)

Pan

sh

arp

ed

im

ag

e b

an

ds

Color models: RGB, CMY

Pan-sharpening

Color models: HSI

Pan-sharpening

Color models: convertion HIS-RGB

Pan-sharpening

Procedure…

1) Convert RGB image composite to HSI

2) Resample (enlarge) HSI image to size of panchromatic band

3) Substitute HSI image intensity band by panchromatic band

4) Convert resulting HSI image to RGB

Pan-sharpening

Alternative methods: PCA

• Principal Component Analysis

• First principal component (PC1): weighted average of all bands

• PC1 represents variations in brightness and intensity

• Procedure:

1) Apply PCA over RGB composite

2) Substitute PC1 by panchromatic band

3) Perform inverse PCA on the result

Pan-sharpening

Alternative methods

• Gram-Schmidt Orthogonalization (Spatial domain)

• Wavelet (Frequency domain)

Pan-sharpening

Pansharp an IKONOS image using the HSI method

1) Download image bands from: http://www.ele.puc-rio.br/~visao/TopicosII/IKONOS_SJC.rar

Remember! Resolutions: pan (1m); red(4m); green(4m); blue(4m)

2) Create pansharped color composites:

R(sjc_red), G(sjc_grn), B(sjc_blu)

R(sjc_nir), G(sjc_red), B(sjc_grn)

Use rgb2hsv, hsv2rgb and imresize functions

3) Compare the 3 interpolation methods: nearest neighborhood, bilinear, bicubic

Pan-sharpening

Radiometric resolution

• Describes the system's ability to discriminate very slight differences in sensed radiance

• The finer the radiometric resolution of a sensor, the more sensitive it is to detecting small differences in reflected or emitted energy


Quantization1bit: 0 and 1

Quantization4bits: 0 and 15

Quantization8bits: 0 and 255

• The number of levels used to express the signal received by a sensor cannot be increased arbitrarily

• Signal to noise ratio (SNR) of the sensor must be taken into consideration

• Step size cannot be less than noise level


Radiometric resolutions:

• Landsat TM: 8 bits

• Landsat ETM: 9 bits

• CBERS-2B HRC: 8bits

• IKONOS-2: 11bits

• Quickbird: 11bits

• Worldview-2: 11bits

• RapidEye: 12 bits

• MODIS: 12 bits


• Resolution: spatial x spectral x radiometric

• High spatial resolution: small IFOV

• Small IFOV: small ground resolution elementarea, small amount of energy reaching the sensor

• Reduces the ability to detect fine energy differences (radiometric resolution): larger SNR

• To increase the amount of energy detected without reducing spatial resolution: broaden the wavelength range detected for a particular channel or band: reduces spectral resolution


Revisit period: time period for a satellite to see the same point over the Earth

Temporal Resolution

• Orbit cycle time: satellite passes over the same point on the Earth's surface directly below the satellite for a second time

• The interval of time required for the satellite to complete its orbit cycle is not the same as the revisit period

• Using steerable sensors, an satellite-borne instrument can view an area (off-nadir) before and after the orbit passes over a target: revisit time less than the orbit cycle time.

• In near-polar orbits, areas at high latitudes will be imaged more frequently than the equatorial zone due to the increasing overlapin adjacent swaths

Temporal Resolution

Landsat 7

Temporal Resolution

• The absolute temporal resolution of a remote sensing system: to image the exact same area at the same viewing angle a second time

• The actual temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the swath overlap, and latitude

MODIS – 1 day

Temporal Resolution

Temporal Resolution

IKONOS2 March 2008 (late Summer) IKONOS2 June 2009 (late Autumn)

Solar Elevation 57,3° Nadir 13,0° Solar Elevation 37,8° Nadir 14,5°

Temporal Resolution

• Collecting imagery of the same area at different periods of time: essential for multitemporal analysis

• Spectral characteristics of features may change over time: detecting change (natural or man made) by comparing multitemporal imagery

• For example: during the growing season, most species of vegetation are in a continual state of change and our ability to monitor those subtle changes using remote sensing is dependent on when and how frequently we collect imagery

Temporal Resolution

• The time factor in imaging is very important when:

• persistent clouds offer limited clear views of the Earth's surface (often in the tropics)

• short-lived phenomena (floods, oil slicks, etc.) need to be imaged

• multitemporal comparisons are required (e.g. the spread of a forest disease from one year to the next)

• the changing appearance of a feature over time can be used to distinguish it from near-similar features (wheat / maize)

Temporal Resolution

Section 2.2 “Characteristics of Imaging Remote Sensing Instruments” of the book “Computer Processing of Remotely-Sensed Images”, Mather & Koch, 4th Edition.

Section 5.2 “General Characteristics of Satellite Remote Sensing Systems Operating in the Optical Spectrum” of the book “Remote Sensing and Image Interpretation”, Lillesand, Kiefer & Chipman, 7th Edition.

Article “Spectral Imaging for Remote Sensing”, Gary A. Shaw and Hsiao-huaK. Burke, Volume 14, Number 1, 2003, MIT Lincoln Laboratory Journal

Lecture Notes

Characteristics of Sensor SystemsMultispectral, Hyperspectral, Thermal Sensing

Raul Q. FeitosaGilson. A. O. P. Costa

Patrick N. Happ

characteristics of sensor systems - puc rio

Documents