1 Basics Of Remote Sensing

Fundamentals of Remote Sensing

Prof. Anjana Vyas School of Planning, CEPT University, Ahmedabad [email protected] (m)098255 22844

Major Components of Remote Sensing Technology: 1). Energy Source: • Passive System: @irradiance from earth's materials; sun, • Active System: @irradiance from artificially generated energy sources such as radar. 2). Platforms: • Vehicle to carry the sensor • e.g. truck, aircraft, space shuttle, satellite, etc. 3). Sensors: • Device to detect electro-magnetic radiation • e.g. camera, scanner, etc.

4). Detectors: • Handling signal data • Photographic, digital, etc. 5). Processing: • Handling Signal data • Photographic, digital etc. 6). Institutionalization: • Organization for execution at all stages of remotesensing technology: • International and national organizations, centers, universities, etc.

Remote Sensing is the science and art of obtaining information about a phenomenon without being in contact with it.

Remote Sensing deals with the detection and measurement of phenomena with devices sensitive to electromagnetic energy.

Perhaps the most novel platform at the end of the last century is the famed pigeon fleet. It operated in Europe.

Remote sensing began in the 1840s as balloonists took pictures of the ground using the newly invented photo-camera.

DATA COLLECTION METHODS

METHODS OF DATA COLLECTION TRADITIONAL METHOD OF GROUND SURVEY ADVANCED AND SOPHISTICATED TECHNIQUE OF REMOTE SENSING

DRAWBACKS OF TRADITIONAL METHOD UNFAVOURABLE WHEATHER INACCESSIBLE AREAS TIME CONSUMING WIDE GAP BETWEEN DATA COLLECTION AND UTILISATION DATA OBSOLETION MONITORING BECOMES UNECONOMICAL

ADVANTAGES OF REMOTE SENSING TECHNIQUE SYNOPTIC VIEW PERMANENT DATA RECORDING UNBIAS DATA REAL TIME DATA MULTI-DISCIPLINARY USE FASTER DATA ACQUISITION & ANALYSIS TEMPORAL AVAILABILITY OF DATA UNIQUE CAPABILITY OF DATA RECORDING – VISIBLE & INVISIBLE – ULTRA VIOLET; REFLECTED INFRARED; THERMAL INFRARED; MICROWAVE; ETC…..

Stages in the Remote Sensing Process

DATA COLLECTION and DATA ANALYSIS

DATA COLLECTION

PLATEFOMS

GROUND BORN AIR BORNE SPACE BORNE

Types of Platforms Platform is a

from 36,000 km

stage to mount the camera or

to

sensor to acquire the information.

1 km height

3-Channel Radiometer taking measurement of Soil and Crops

A reflectance spectrometer mounted on a truck’s cherry picker

Automated Data collection platform instrumented to provide data on stream flow characteristics

Hand sized GPS instruments

NORMAL COLOUR PHOTOGRAPH

FALSE COLOUR PHOTOGRAPH

FIND THE DIFFERENCE!!!

Lava Observed on the face of Kilauea Volcano, Jan. 22, 1971;12:15pm

Normal Color

Lava Observed on the face of Kilauea Volcano, Jan. 22, 1971;12:15pm

Color infrared • The orange tone – infrared energy emitted from the flowing lava • The pink tones – sunlight reflected from the living

hotographs showing lava on the face of Kilauea Volcano, nuary 22, 1971, 12:15 PM Normal Color

Color infrared • The orange tone – infrared energy emitted from the flowing lava • The pink tones – sunlight reflected from the living

Simulated normal color Photograph

Simulated color infrared Photograph

SPACE BORNE

SATELLITE AS OBSERVED

IKONOS Satellite

QuickBird Satellite

2003 IRS-1C (1995) LISS-3 (23/70M, STEERABLE PAN (5.8 M); WiFS (188M)

IRS-P2 (1994) LISS-2

IRS-1D (1997) LISS-3 (23/70M, STEERABLE PAN (5.8 M); WiFS (188M)

IRS-P3 (1996) WiFS MOS X-Ray, IRS-P4 (1999) OCEANSAT OCM, MSMR

RESOURCESAT-1 LISS3 - 23 M; 4 XS LISS4 - 5.8 M; 3-XS AWIFS - 70 M; 4-XS

CARTOSAT - 1 PAN - 2.5M, 30 KM, F/A

2004

CARTOSAT-2 PAN - 1M

IRS-1A & 1B ( 1988 & 91) LISS-1&2 (72/36M, 4 BANDS; VIS & NIR)

IRS IRS SERIES SERIES

2003

2005 MEGHA-TROPIQUES SAPHIR SCARAB & MADRAS

Seven Elements of Remote Sensing A. Energy Source or Illumination

Seven Elements of Remote Sensing B. Radiation & Atmosphere

Seven Elements of Remote Sensing C. Interaction with Target

Seven Elements of Remote Sensing D. Recording of Energy by the Sensor

Seven Elements of Remote Sensing E. Transmission, Reception, and Processing

Seven Elements of Remote Sensing F. Interpretation And Analysis

Seven Elements of Remote Sensing G. Application

Our eyes can directly perceive only a small portion of the electromagnet ic spectrum (EMS).

We can gather more information by using other portions of the EMS, such as infrared.

SATELLITE CHARACTERISTICS

GEOSTATIONARY SATELLITE ORBIT

sunsynchr onous

SWATH

REVISIT PATH

Ascending and Descending Passes

SWATH OF ADJACENT PATH

SWATH OF ADJACENT PATH

Latitude

Indian Remote Sensing Satellite In 24hrs satellite makes 13.9545 revolutions around the earth. The orbit on the second day (15th orbit) is shifted westward from orbit No.1 by about 130 km. The ground traces repeat after every 307 orbits in 22 days.

11

10

9

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15 Orbit Number

Longitude

1

14

13

12

IKONOS- Maha Kumbh

IKONOS- Airport

IKONOS- Statue of Liberty

RESOLUTON

Pixel: “picture element”

Pixel size determines resolution

82,000 Tiny Pixels

88 Giant Pixels

File size: 99.3 kb

File size: 4.1 kb

How much resolution you need depends upon how much detail you need. More resolution means more information: more data to look at, but also more to store.

RESOLUTIONS SPATIAL RADIOMETRIC SPECTRAL TEMPORAL

SPATIAL RESOLUTION 1.COARSE OR LOW 2. FINE OR HIGH

COARSE OR LOW RESOLUTION

FINE OR HIGH RESOLUTION

SPECTRAL RESOLUTION

Multisp ectral 20m

Panchro matic 10m

Merged (multispectral +panchromati c)

Simulated SPOT Images, Sherbrooke, Quebec June 1982 Multisp ectral 20m

Panchro matic 10m

Merged (multispectral +panchromati c)

IRS LISS II 36.25 m

IRS LISS III 23.5 m

IRS PAN 5.8 m

TEMPORAL RESOLUTION

Temporal Resolution

Satellite

IRS 1C

IRS 1D

Sensors IRS-1C & IRS-1D

Instrument

image

Resolution

Swath (km)

PAN

5.6

70

LISS 3

23

142-148

WiFS

188

774

Scenes

PAN

Band Wavelength Region (µm) Resolution (m)

1 0.50-0.75 6

LISS III

Band Wavelength Region (µm) 1

0.52 - 0.59 (green)

2

0.62 - 0.68 (red)

3

0.77 - 0.86 (near-IR)

4

1.55 - 1.70 (mid-IR)

WiFS

Band

Wavelength Region (µm)

140 km

Resolution (m)

10.62-0.68 (red)

188

20.77-0.86 (near-IR)

188

VISUAL IMAGE INTERPRETATION

Image Interpretation Image Interpretation Key: •Shape •Size •Shadow •Pattern •Tone •Texture •Association •Location

Built Up land - Urban Tone: Cyan Texture: Coarse Size: Variable Shape: Variable Shadow: Observed Pattern: Regular Association: Mixed with building

LISS III

Built Up land - Urban

PAN

Built Up land – Settlement

Built Up Land -- ????? Tone: White Texture: Medium Size: Variable Shape: Rectangle Shadow: Pattern: Regular Association: with Open land

Air Strip

Built Up land –Transportation -- Road

Tone: Dark Grey Texture: Medium Size: Variable Shape: Straight with bends Shadow: Pattern: Linear Association: with pink tone

Agricultural Land -- Crop Land

Tone: Pinkish Red Texture: Fine Size: Variable Shape: Variable Shadow: Pattern: Scattered Association:

Agricultural Land -- Fallow

Tone: Whitish blue Texture: Coarse Size: Variable Shape: Variable Shadow: Pattern: Scattered Association:

Agricultural Land – ?????

Tone: Dark pink Texture: Smooth Size: Variable Shape: Variable Shadow: Pattern: Scattered Association:

Water -- River

Tone: Dark Blue

Texture: Smooth

Size: Variable

Shape: Variable

Shadow:

Pattern: Scattered

Water -- Reservoir

Water-- Reservoir -- Canal

Range Land

Wasteland -- Ravines

Waste Land -- Beaches -- Mud Flats

Tone: Light Cyan with pink Texture: Coarse Size: Variable Shape: Variable Shadow: Pattern: scattered Association:

Waste Land - Coastal features

Waster Land -- Saltpan

Tone: Bright White Texture: Smooth Size: Uniform Shape: Rectangle Shadow: Pattern: contiguous Association:

Waste Land – Rock/Strip Mines/ Stone Quarries

Tone: Whitish cyan

Texture: Coarse

Size: Variable

Shape: Variable

Shadow:

Pattern: Scattered

Geological features

Hills

?????

Any Questions?

Survey of India Toposheets

SHEET NO. 63 A/7 63 A/2

63 A/6

63 A/10

63 A/3

63 A/7

63 A/11

63 A/4

63 A/8

63 A/12

63A 1:250000

A

E

I

M

B

F

J

N

63 C

G

K

O

D

H

L

P

1

5

9

13

2

6

10

14

63 3

7

11

15

4

8

12

16

SOI Map Indexing

E 56 A

I

M

( 1 deg x 1 deg)

B C

F G

J 1

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3 4

D

H

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8

NW

NE

SW

SE

O P

Indexing scheme

1:50 000

1:250 000

1: 100 000 1:25 000

Features as seen from satellite image

Features Names Airport

Different shapes of Building

Chimney

Light House Town

Their symbols on Toposheet

Masonry/Earth fill Dams Mosque/Temples Overhead Tanks Walls Play grounds etc. Ponds Perennial

Dry

Roads: Metalled Roads: Unmetalled Cart-Track & Footpath with Bridge Bridge: with piere; without Causeway & ford/ferry Railway: broad-gauge: Double; single with station; under construction Railway: other gauge: Double; single with station; under construction Cutting with tunnel

ADMINISTRATIVE INDEX HARDOI

SITAPUR

Map sources Mapping Agency Scale Survey of India

25 000 50 000 250 000

Remarks 40% coverage –10 m CI Entire India – 20, 40m,.. CI Compiled from 50K maps ** Everest, Polyconic

Mil. Survey of India

50 000

Inputs from various sources; Satellite data based updation ** Everest, Polyconic, Mil Grid

Russian maps

10 000 100 000 200 000

Limited coverage 40 m contours 80 m contours ** Krassovisky, TM

DCW from ESRI

1M

Sourced from ONC maps 1000 ft CI

So Get Set for the Hands On

Digital Image Processing DIP is a: Computer-based manipulation and interpretation of digital images.

Digital Image Processing Five broad types of computer assisted operation: 1.Image rectification & restoration (preprocessing) 2.Image enhancement 3.Image classification 4.Data merging and GIS integration 5.Hyperspectral image analysis

Image Rectification & Restoration Preprocessing to correct distorted or degraded image data – Geometric distortions – Radiometric calibration – Elimination of noise

Image Enhancement To more effectively display or record the data, increasing the visual distinctions between features in a scene Contrast manipulation: stretching

Image Enhancement Spatial feature manipulation: – Filtering – Convolution – Edge enhancement – Fourier

Enhancement involving multiple bands of an image

Image Classification The overall objective of classification is to categorize all pixels in a digital image into one of several land cover classes Themes  thematic maps

Supervised Classification Common Classifiers: – Parallelpiped – Minimum distance to mean – Maximum likelihood

Supervised Classification Supervised classification requires the analyst to select training areas where he/she knows what is on the ground The computer then creates... and then digitize a polygon within that area…

Mean Spectral Signatures Conifer

Known Conifer Area

Water

Known Water Area

Deciduous

Known Deciduous Area Digital Image

Supervised Classification Mean Spectral Signatures

Multispectral Image

Information (Classified Image)

Conifer

Deciduous

Water

Unknown

Spectral Signature of Next Pixel to be Classified

The Result is Information--in this case a Land Cover map...

Land Cover Map Legend: Water Conifer Deciduous

Supervised Classification Parallelepiped Approach Pros:

40 35 30 Band 4

– Simple – Makes few assumptions about character of the classes

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25 20 15 10 5 0 0

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10 Band 3

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Supervised Classification: Statistical Approaches Minimum distance to mean

40 35 30 Band 4

– Find mean value of pixels of training sets in ndimensional space – All pixels in image classified according to the class mean to which they are closest

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25 20 15 10 5 0 0

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10 Band 3

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Supervised Classification: Minimum Distance Minimum distance – Pros: All regions of n-dimensional space are classified Allows for diagonal boundaries (and hence no overlap of classes)

Supervised Classification: Maximum Likelihood Maximum likelihood classification: another statistical approach Assume multivariate normal distributions of pixels within classes For each class, build a discriminant function – For each pixel in the image, this function calculates the probability that the pixel is a member of that class – Takes into account mean and covariance of training set Each pixel is assigned to the class for which it has the highest probability of membership

Maximum Likelihood Classifier Relative Reflectance

Mean Signature 1 Candidate Pixel Mean Signature 2

It appears that the candidate pixel is closest to Signature 1. However, when we consider the variance around the signatures…

Blue

Green

Red

Near-IR

Mid-IR

Maximum Likelihood Classifier Relative Reflectance

Mean Signature 1 Candidate Pixel Mean Signature 2

The candidate pixel clearly belongs to the signature 2 group.

Blue

Green

Red

Near-IR

Mid-IR

Supervised Classification Maximum likelihood – Pro: Most sophisticated; achieves good separation of classes

– Con: Requires strong training set to accurately describe mean and covariance structure of classes

GCPs

Resample

Lat & Long

Thanking You All…