This document provides an overview of fundamentals of remote sensing. It begins by defining remote sensing as acquiring information about an object without physical contact. It then discusses various aspects of remote sensing including the basic components of a remote sensing system, examples of early and modern remote sensing applications, different sensor types and resolutions. The document also covers topics such as raster data models, file formats for raster data, imagery types, preprocessing techniques including radiometric and geometric corrections, image enhancement methods, image classification approaches, and principles of image interpretation.

1. Fundamentals Remote
Sensing
The science and art of acquiring of information about an object without being in physical contact with it
Dr. Nishant Sinha

2. Remote Sensing
As you view the screen of your
computer monitor, you are actively
engaged in remote sensing.
HOW ?
THE ANSWER IS
A physical quantity (light) emanates
from the screen, which is a source of
radiation.The radiated light passes
over a distance, and thus is "remote" to
some extent.

3. Remote Sensing
1
2
3 5
6
1. energy source
2. atmospheric
interaction
3. ground object
4. data recording /
transmission
5. ground receiving
station
6. data processing
7. expert interpretation
/ data users
4
7

5. Balloon Remote Sensing, Paris, 1858

6. Pigeon Remote Sensing

7. Actual Pigeon Pictures

8. Wilbur Wright and his
first aerial photograph of
France.

10. Apollo Spacecraft Mission

11. Satellite Remote Sensing

14. LandSAT Satellite
Ox-Bow of the
Mississippi

15. Indian Remote
Sensing (IRS) Satellite
Bihar State Map
on AWiFS Data

17. Remote Sensing Sensors

18. True color film
Infrared film

19. Landsat Images

20. Quickbird Image

21. ▪ Area is covered by grid with (usually) equal-sized cells
▪ Cells often called pixels (picture elements); raster data often called image data
▪ Attributes are recorded by assigning each cell a single value based on the
majority feature (attribute) in the cell, such as land use type.
▪ Easy to do overlays/analyses, just by ‘combining’ corresponding cell values:
“yield= rainfall + fertilizer” (why raster is faster, at least for some things)
▪ Simple data structure:
– directly store each layer as a single table
(basically, each is analagous to a “spreadsheet”)
– computer data base management system not required
(although many raster GIS systems incorporate them)
Representing Data using Raster Model
corn
wheat
fruit
clover
fruit
oats

22. Raster Array Representations
▪ Raster data comprises rows and columns, by one or more characteristics or arrays
– elevation, rainfall, & temperature; or multiple spectral channels (bands) for remote sensed data
– how organise into a one dimensional data stream for computer storage & processing?
▪ Band Sequential (BSQ)
– each characteristic in a separate file
– elevation file, temperature file, etc.
– good for compression
– good if focus on one characteristic
– bad if focus on one area
▪ Band Interleaved by Pixel (BIP)
– all measurements for a pixel grouped together
– good if focus on multiple characteristics of geographical area
– bad if want to remove or add a layer
▪ Band Interleaved by Line (BIL)
– rows follow each other for each characteristic
File 1: Veg A,B,B,B
File 2: Soil I,II,III,IV
File 3: El. 120,140,150,160
A,I,120, B,II,140 B,III,150 B,IV,160
A,B,I,II,120,140 B,B,III,IV,150,160
Note that we start in lower left.
Upper left is alternative.
A B
B B
III IV
I II 150 160
120 140
Elevation
Soil
Veg

23. The generic raster data model is actually implemented in several different computer file
formats:
▪ GRID is ESRI’s proprietary format for storing and processing raster data
▪ Standard industry formats for image data such as JPEG, TIFF and MrSid formats can be
used to display raster data, but not for analysis (must convert to GRID)
▪ Georeferencing information required to display images with mapped vector data
– Requires an accompanying “world” file which provides locational information
File Formats for Raster Spatial Data
Image Image File World File
TIFF image.tif image.tfw
Bitmap image.bmp image.bpw
BIL image.bil image.blw
JPEG image.jpg image.jpw

24. Remote Sensing: Imagery Types
TES
1 m
Quickbird
61cm
IRS 1D
23.5 m
IRS 1D
5.8 m
High Resolution Imagery Low Resolution Imagery
Panchromatic Imagery Multi-spectral Imagery

25. Resolution
▪ The Ability to Discriminate
▪ Types of Resolution
– Spatial: Discrimination by Distance
– Spectral: Discrimination by Wave length
– Radiometric: Discrimination by energy levels
– Temporal: Discrimination byTime
5.8m
5.8m
Radiometric
Resolution
8-bit (0-255)
Spectral
Resolution
0.4-0.7 μm
Day 1
Day 48
Day 96

26. What is an image?
▪ Data that are organized in a grid of columns and rows
▪ Usually represents a geographical area
X-axis

27. An image refers to any pictorial
representation, regardless of what
wavelengths or remote sensing device has
been used to detect and record the
electromagnetic energy.
A photograph refers specifically to images
that have been detected as well as
recorded on photographic film.
Based on these definitions, we can say that
all photographs are images, but not all
images are photographs.
Difference between Image and Photographs

28. Pixels
▪ Resulting images are made of a grid of
pixels
• Each pixel stores a digital number (DN)
measured by the sensor
• Represents individual areas scanned by
the sensor
• The smaller the pixel, the easier it is to
see detail

29. Continuous data
Two types:
• Panchromatic ( 1 Band/layer)
• Multispectral ( 2 or more Bands)

30. Viewing continuous images
▪ Each band or layer is viewable as a separate image
Thematic Mapper Band 1
Band 4
Band 5

31. Blue
Green
Red
NIR
SWIR
Part of
spectrum
Monitor
color guns
Viewing images
▪ Three bands are viewable simultaneously
Band
4
Band
3
Band
2
Band
4
Band
5
Band
3
Band
1
Band
2
Band
3

32. Geomteric Corrections
▪ All remote sensing imagery inherently subject to geometric distortions
caused by various factors
▪ Geometric corrections intended to compensate for these distortions
▪ Required so that geometric representation of the imagery is as close as
possible to the real world
▪ Geometric registration of the imagery to a known ground coordinate
system must be performed

33. ▪ Radiometric Corrections
– changing the image data BVs to correct for errors or distortions
▪ atmospheric effects (scattering and absorption)
▪ sensor errors
▪ GeometricCorrections
– changing the geometric/spatial properties of the image data
– Also called image rectification or rubber sheeting
Image Preprocessing

34. Geometric Registration
▪ Image-to-map registration
– Involves identifying the image coordinates (i.e. row, column) of
several clearly discernible points, called ground control points (or
GCPs), in the distorted image (A - A1 to A4), and matching them to
their true positions in ground coordinates (e.g. latitude, longitude).
– True ground coordinates are measured from a map (B - B1 to B4),
either in paper or digital format
▪ Image-to-image registration
– Performed by registering one (or more) images to another image,
instead of geographic coordinates
▪ Several types of transformations applied on image co-
ordinates to transform into real world coordinates:
– Plane transformations - keep lines straight, being on the first order
– Curvilinear (polynomial) - higher order transformations that do not
necessarily keep lines straight and parallel
– Triangulation.
– Piecewise transformations - Break the map into regions, apply
different transformations in each region

35. Geometric Corrections
▪ All remote sensing imagery inherently subject to geometric distortions
caused by various factors
▪ Geometric corrections intended to compensate for these distortions
▪ Required so that geometric representation of the imagery is as close as
possible to the real world
▪ Geometric registration of the imagery to a known ground coordinate
system must be performed

36. ▪ Distortions factors
– the perspective of the sensor
optics
– the motion of the scanning
system
– the motion of the platform
– the platform altitude
– attitude, and velocity
– the terrain relief and
– the curvature and rotation of
the Earth
▪ Distortions type
– Systematic (predictable in
nature)
▪ Accounted through
accurate modeling of sensor
and platform motion and
▪ Geometric relationship of
the platform with the Earth
– Unsystematic (random)
errors cannot be modeled
and corrected
Earth Rotation AltitudeVariation PitchVariation
SpacecraftVelocity RollVariation YawVariation
Non
Systematic
Distortions
Systematic
Distortions
Image distortions
Scanner distortions
Actual
Velocity
Nominal
Velocity
Mirror
Angle
time
Mirror velocity variations Scan Skew

37. Geometric Correction
▪ Four Basic Steps of Rectification
1. Collect ground control points (GCPs)
2. “Tie” points on the image to GCPs.
3. Transform all image pixel coordinates using mathematical
functions that allow “tied” points to stay correctly mapped to GCPs.
4. Resample the pixel values (BVs) from the input image to put values
in the newly georeferenced image

38. Geometric Correction
▪ ThreeTypes of Resampling
– Nearest Neighbor - assign the
new BV from the closest input
pixel.This method does not
change any values
– Bilinear Interpolation - distance-
weighted average of the BVs
from the 4 closest input pixels
– Cubic Convolution - fits a
polynomial equation to
interpolate a “surface” based on
the nearest 16 input pixels; new
BV taken from surface
1
2
3
4
1
2
3
4

39. Image Enhancements
▪ Procedures of making a raw image more interpretable for a particular
application
▪ Improve the visual impact of the raw remotely sensed data on the human
eye
▪ Classification
– Contrast (global) enhancement: Transforms raw data using statistics computed over
whole data set
▪ Examples - Linear contrast, histogram equalized and piece-wise contrast stretch
– Spatial (local) enhancement - Local conditions considered only that vary over image
▪ Examples - Image smoothing and sharpening

40. Image Enhancements
▪ Procedures of making a raw image more
interpretable for a particular application
▪ Improve the visual impact of the raw
remotely sensed data on the human eye
▪ Contrast (global) enhancement:
Transforms raw data using statistics
computed over whole data set (Examples -
Linear contrast, histogram equalized and
piece-wise contrast stretch)
▪ Spatial (local) enhancement - Local
conditions considered only that vary over
image (Examples - Image smoothing and
sharpening)

41. Image Enhancement: Example
▪ Contrast Enhancement - “stretching” all or part of input BVs from
the image data to the full 0-255 screen output range

43. Image Fusion
LISS III PAN
Brovey Multiplicative PCA Wavelet

44. Image Classification
▪ To label the pixels in the image with meaningful information of the
real world.
▪ Classification of complex structures from high resolution imagery
causes obstacles due to their spectral and spatial heterogeneity
▪ Two types
– Unsupervised classification
– Supervised classification
44

45. Supervised vs. Unsupervised Approaches
– Unsupervised: statistical "clustering" algorithms used to
select spectral classes inherent to the data, more
computer-automated
Posterior Decision
– Supervised: image analyst "supervises" the selection of
spectral classes that represent patterns or land cover
features that the analyst can recognize
Prior Decision

46. Edit/evaluat
e signatures
SelectTraining
fields
Classify
image
Evaluate
classification
Identify
classes
Run clustering
algorithm
Evaluate
classification
Edit/evaluate
signatures
Supervised Unsupervisedvs

47. Image Enhancements
▪ Procedures of making a raw image more interpretable for a particular
application
▪ Improve the visual impact of the raw remotely sensed data on the human
eye
▪ Classification
– Contrast (global) enhancement: Transforms raw data using statistics computed over
whole data set
▪ Examples - Linear contrast, histogram equalized and piece-wise contrast stretch
– Spatial (local) enhancement - Local conditions considered only that vary over image
▪ Examples - Image smoothing and sharpening

48. Band Combinations
▪ Features can become more obvious
Vegetation
Urban
2,3,1 (RGB) 4,3,2 (RGB)4,5,3 (RGB)

49. Keys to Image Interpretation
▪ Shape
▪ Size
▪ Shadows
▪ Tone
▪ Color
▪ Texture
▪ Pattern

50. Interpretation Principles
• Shape
• Size
• Shadow
• Tone/Color
• Texture
• Pattern
• Relationship to Surrounding Objects

51. ▪ The photo on the right is a black
and white photo of the City of
Ithaca and the Cornell
University campus taken in
1991. More specifically, it was
taken on April 4, 1991 (look in
the upper left hand corner).
▪ So lets take a quick tour of the
photograph
Image Interpretation

52. ▪ Size: the size of an object is one of the
most useful clues to its identity. Also,
understanding the size of one object
may help us understand the sizes of
other objects.
▪ For example, most of us have a feeling
for the size of a baseball field, and
football field. When we observe these
objects on a photograph, it will help us
to understand the sizes of other
objects on the photograph.
▪ For example, on another part of the
photograph we have a trailer park.
This could easily be confused with a
parking lot, but when we understand
the size of the objects we will realize
that the objects in the trailer park are
much too large to be cars.
Image Interpretation

53. ▪ Shape: Shapes can often give away
an object’s identity. For example, a
cloverleaf is a very distinctive
feature of a highway, while a
stream’s meandering gives away its
identity.
▪ And again, the baseball diamond
we just looked at also has a
distinctive shape.
Image Interpretation

54. ▪ Shadow: shadows often give us an
indication of the size and shape of
an object. When we look at aerial
photographs we often see a
vantage point we are not used to:
an overhead view.
▪ Shadows can let us “cheat” alittle
to see the side of an object. The
photos on the right show the
CornellTheory Center, which casts
a rather large shadow, indicating
the building size, and a water tower
on one of the farms on campus. If
you look closely, you can see the
“legs” of the watertower.
Image Interpretation

55. ▪ Shadow: while shadows are
helpful, they can also be a
hindrance. As we try to look down
into the gorge on the Cornell
campus, we can see very little due
to the shadows cast.
Image Interpretation

56. ▪ Tone:You can see the tonal
contrast between Cayuga Lake and
the land area. Also, there is good
tone representation for wet or dry
soils.
Image Interpretation

57. ▪ Texture: In this photo we see the Cornell
Plantations and Botanical Garden, as well
as the experimental agricultural plots.
Especially in the Plantations, you will see
the different textural characteristics
between the mowed lawns and the grassy
areas. Notice too, the small pond in the
Plantations (an example of tone)
▪ Additionally, around another natural area
on campus you can see the textural
difference of trees vs. more of a grassland
area.
▪ And again, as you look at the agricultural
plots you will notice a different texture
from the forested areas.
▪ Finally, in the golf course shown below
there are obvious patterns between
managed lawns vs. the unmanaged lawns,
in addition to the tonal differences
between the lawns and sand traps.
Image Interpretation

58. ▪ Pattern:There are so many
examples related to pattern. These
would include the rectilinear
pattern of the older, urban
neighborhoods in Ithaca, the
straight lines of trees in an orchard,
the rectilinear shape of the
experimental agricultural plots, and
the configuration of a parking lot.
▪ Also, the pattern of the golf course
with greens, tees, traps, and
fairways is very easy to spot.
Image Interpretation

59. ▪ Pattern: the drainage
pattern for a particular
property on this photo is
easy to see. Also, because
the drainage is relatively
straight, we can assume
that a moderate to steep
slope exists, as water did
not have much opportunity
to meander.
Image Interpretation

60. ▪ Relationship: observing relationships on
photographs is one of the most fun
observations. For example, a school and a
plaza are interpreted differently due to
relationships:
– While both have many large structures on
them, schools typically have playing fields
– Also, plazas usually have larger parking
areas
▪ Here we see the East Hill Shopping Plaza
(no athletic fields, but a campus of
buildings), and the Ithaca High School
campus (with athletic fields)
Image Interpretation

61. ▪ Relationship: here is another example of
relationship that shows a middle school
and an elementary school. Notice that it
have buildings like the high school, and a
parking lot, but no real athletic fields to
speak of. What it does have, however, is
what appears to be a playground, and is
surrounded by a residential community.
▪ The structures on the top are an
apartment complex. They could be
tractor trailers, but “size” gives them
away.They are too large to be tractor
trailers when you consider the size of the
schools below.
▪ Notice that just north of the apartment
complex is a large pool. How do we
know it’s a pool, well, the tone gives us a
clue…
Image Interpretation
Apartments
School
School

62. ▪ interpretation is putting together our
observations bit by bit to form a coherent
understanding of the image. For
instance, identifying the water treatment
plant forces us to use
shape, pattern, tone, and relationship to
make the connection:
– We see the water holding areas in
black (tone)
– We see the large tanks (shape)
– And when you’ve seen one treatment
plant, you’ve seen them all (pattern)!!
▪ Notice that across the water is a park.
Why do we know it’s a park?
Well, again, we see multiple ball
fields, not enough buildings to be a
school, and a very large pool.
Image Interpretation

63. Acknowledgement
These slides are aggregations for better understanding of GIS. I acknowledge the
contribution of all the authors and photographers from where I tried to
accumulate the info and used for better presentation.

64. Author’s Coordinates:
Dr. Nishant Sinha
Pitney Bowes Software, India
mr.nishantsinha@gmail.com