0
Fundamentals Remote
Sensing
The science and art of acquiring of information about an object without being in physical cont...
Remote Sensing
As you view the screen of your
computer monitor, you are actively
engaged in remote sensing.
HOW ?
THE ANSW...
Remote Sensing
1
2
3 5
6
1. energy source
2. atmospheric
interaction
3. ground object
4. data recording /
transmission
5. ...
Balloon Remote Sensing, Paris, 1858
Pigeon Remote Sensing
Actual Pigeon Pictures
Wilbur Wright and his
first aerial photograph of
France.
Apollo Spacecraft Mission
Satellite Remote Sensing
LandSAT Satellite
Ox-Bow of the
Mississippi
Indian Remote
Sensing (IRS) Satellite
Bihar State Map
on AWiFS Data
Remote Sensing Sensors
True color film
Infrared film
Landsat Images
Quickbird Image
▪ Area is covered by grid with (usually) equal-sized cells
▪ Cells often called pixels (picture elements); raster data oft...
Raster Array Representations
▪ Raster data comprises rows and columns, by one or more characteristics or arrays
– elevatio...
The generic raster data model is actually implemented in several different computer file
formats:
▪ GRID is ESRI’s proprie...
Remote Sensing: Imagery Types
TES
1 m
Quickbird
61cm
IRS 1D
23.5 m
IRS 1D
5.8 m
High Resolution Imagery Low Resolution Ima...
Resolution
▪ The Ability to Discriminate
▪ Types of Resolution
– Spatial: Discrimination by Distance
– Spectral: Discrimin...
What is an image?
▪ Data that are organized in a grid of columns and rows
▪ Usually represents a geographical area
X-axis
An image refers to any pictorial
representation, regardless of what
wavelengths or remote sensing device has
been used to ...
Pixels
▪ Resulting images are made of a grid of
pixels
• Each pixel stores a digital number (DN)
measured by the sensor
• ...
Continuous data
Two types:
• Panchromatic ( 1 Band/layer)
• Multispectral ( 2 or more Bands)
Viewing continuous images
▪ Each band or layer is viewable as a separate image
Thematic Mapper Band 1
Band 4
Band 5
Blue
Green
Red
NIR
SWIR
Part of
spectrum
Monitor
color guns
Viewing images
▪ Three bands are viewable simultaneously
Band
...
Geomteric Corrections
▪ All remote sensing imagery inherently subject to geometric distortions
caused by various factors
▪...
▪ Radiometric Corrections
– changing the image data BVs to correct for errors or distortions
▪ atmospheric effects (scatte...
Geometric Registration
▪ Image-to-map registration
– Involves identifying the image coordinates (i.e. row, column) of
seve...
Geometric Corrections
▪ All remote sensing imagery inherently subject to geometric distortions
caused by various factors
▪...
▪ Distortions factors
– the perspective of the sensor
optics
– the motion of the scanning
system
– the motion of the platf...
Geometric Correction
▪ Four Basic Steps of Rectification
1. Collect ground control points (GCPs)
2. “Tie” points on the im...
Geometric Correction
▪ ThreeTypes of Resampling
– Nearest Neighbor - assign the
new BV from the closest input
pixel.This m...
Image Enhancements
▪ Procedures of making a raw image more interpretable for a particular
application
▪ Improve the visual...
Image Enhancements
▪ Procedures of making a raw image more
interpretable for a particular application
▪ Improve the visual...
Image Enhancement: Example
▪ Contrast Enhancement - “stretching” all or part of input BVs from
the image data to the full ...
Image Fusion
LISS III PAN
Brovey Multiplicative PCA Wavelet
Image Classification
▪ To label the pixels in the image with meaningful information of the
real world.
▪ Classification of...
Supervised vs. Unsupervised Approaches
– Unsupervised: statistical "clustering" algorithms used to
select spectral classes...
Edit/evaluat
e signatures
SelectTraining
fields
Classify
image
Evaluate
classification
Identify
classes
Run clustering
alg...
Image Enhancements
▪ Procedures of making a raw image more interpretable for a particular
application
▪ Improve the visual...
Band Combinations
▪ Features can become more obvious
Vegetation
Urban
2,3,1 (RGB) 4,3,2 (RGB)4,5,3 (RGB)
Keys to Image Interpretation
▪ Shape
▪ Size
▪ Shadows
▪ Tone
▪ Color
▪ Texture
▪ Pattern
Interpretation Principles
• Shape
• Size
• Shadow
• Tone/Color
• Texture
• Pattern
• Relationship to Surrounding Objects
▪ The photo on the right is a black
and white photo of the City of
Ithaca and the Cornell
University campus taken in
1991....
▪ Size: the size of an object is one of the
most useful clues to its identity. Also,
understanding the size of one object
...
▪ Shape: Shapes can often give away
an object’s identity. For example, a
cloverleaf is a very distinctive
feature of a hig...
▪ Shadow: shadows often give us an
indication of the size and shape of
an object. When we look at aerial
photographs we of...
▪ Shadow: while shadows are
helpful, they can also be a
hindrance. As we try to look down
into the gorge on the Cornell
ca...
▪ Tone:You can see the tonal
contrast between Cayuga Lake and
the land area. Also, there is good
tone representation for w...
▪ Texture: In this photo we see the Cornell
Plantations and Botanical Garden, as well
as the experimental agricultural plo...
▪ Pattern:There are so many
examples related to pattern. These
would include the rectilinear
pattern of the older, urban
n...
▪ Pattern: the drainage
pattern for a particular
property on this photo is
easy to see. Also, because
the drainage is rela...
▪ Relationship: observing relationships on
photographs is one of the most fun
observations. For example, a school and a
pl...
▪ Relationship: here is another example of
relationship that shows a middle school
and an elementary school. Notice that i...
▪ interpretation is putting together our
observations bit by bit to form a coherent
understanding of the image. For
instan...
Acknowledgement
These slides are aggregations for better understanding of GIS. I acknowledge the
contribution of all the a...
Author’s Coordinates:
Dr. Nishant Sinha
Pitney Bowes Software, India
mr.nishantsinha@gmail.com
Fundamentals of Remote Sensing- A training module
Fundamentals of Remote Sensing- A training module
Fundamentals of Remote Sensing- A training module
Fundamentals of Remote Sensing- A training module
Fundamentals of Remote Sensing- A training module
Fundamentals of Remote Sensing- A training module
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Fundamentals of Remote Sensing- A training module

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Fundamentals of Remote Sensing and Digital Image Processing

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Transcript of "Fundamentals of Remote Sensing- A training module"

  1. 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. 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. 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
  4. 4. Balloon Remote Sensing, Paris, 1858
  5. 5. Pigeon Remote Sensing
  6. 6. Actual Pigeon Pictures
  7. 7. Wilbur Wright and his first aerial photograph of France.
  8. 8. Apollo Spacecraft Mission
  9. 9. Satellite Remote Sensing
  10. 10. LandSAT Satellite Ox-Bow of the Mississippi
  11. 11. Indian Remote Sensing (IRS) Satellite Bihar State Map on AWiFS Data
  12. 12. Remote Sensing Sensors
  13. 13. True color film Infrared film
  14. 14. Landsat Images
  15. 15. Quickbird Image
  16. 16. ▪ 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
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. What is an image? ▪ Data that are organized in a grid of columns and rows ▪ Usually represents a geographical area X-axis
  22. 22. 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
  23. 23. 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
  24. 24. Continuous data Two types: • Panchromatic ( 1 Band/layer) • Multispectral ( 2 or more Bands)
  25. 25. Viewing continuous images ▪ Each band or layer is viewable as a separate image Thematic Mapper Band 1 Band 4 Band 5
  26. 26. 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
  27. 27. 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
  28. 28. ▪ 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
  29. 29. 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
  30. 30. 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
  31. 31. ▪ 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
  32. 32. 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
  33. 33. 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
  34. 34. 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
  35. 35. 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)
  36. 36. Image Enhancement: Example ▪ Contrast Enhancement - “stretching” all or part of input BVs from the image data to the full 0-255 screen output range
  37. 37. Image Fusion LISS III PAN Brovey Multiplicative PCA Wavelet
  38. 38. 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
  39. 39. 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
  40. 40. Edit/evaluat e signatures SelectTraining fields Classify image Evaluate classification Identify classes Run clustering algorithm Evaluate classification Edit/evaluate signatures Supervised Unsupervisedvs
  41. 41. 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
  42. 42. Band Combinations ▪ Features can become more obvious Vegetation Urban 2,3,1 (RGB) 4,3,2 (RGB)4,5,3 (RGB)
  43. 43. Keys to Image Interpretation ▪ Shape ▪ Size ▪ Shadows ▪ Tone ▪ Color ▪ Texture ▪ Pattern
  44. 44. Interpretation Principles • Shape • Size • Shadow • Tone/Color • Texture • Pattern • Relationship to Surrounding Objects
  45. 45. ▪ 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
  46. 46. ▪ 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
  47. 47. ▪ 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
  48. 48. ▪ 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
  49. 49. ▪ 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
  50. 50. ▪ 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
  51. 51. ▪ 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
  52. 52. ▪ 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
  53. 53. ▪ 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
  54. 54. ▪ 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
  55. 55. ▪ 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
  56. 56. ▪ 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
  57. 57. 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.
  58. 58. Author’s Coordinates: Dr. Nishant Sinha Pitney Bowes Software, India mr.nishantsinha@gmail.com
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