This document provides an introduction to digital image interpretation. It discusses what digital images are, how they can be displayed in color composites, and how surface features typically appear on true and false color composites. It also outlines the main steps in digital image processing, including preprocessing, enhancement, transformation, and classification. Preprocessing operations like radiometric and geometric corrections are described in detail. Methods for image registration, resampling, and spatial filtering are also explained. Spatial filters can be used for tasks like edge detection, image smoothing, and enhancing linear features. Examples demonstrate the effects of low-pass filtering for speckle removal and high-pass edge detection.
IMAGE INTERPRETATION TECHNIQUES of surveyKaran Patel
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
IMAGE INTERPRETATION TECHNIQUES of surveyKaran Patel
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
IMAGE INTERPRETATION
Act of examining images to identify objects and judge their significance.
Information extraction process from the images.
An interpreter is a specialist trained in study of photography or imagery, in addition to his own discipline.
Aerial photographs and remote Sensing images employ electro magnetic energy as the mean of detecting and measuring target characteristics.
Involves a considerable amount of subjective judgment.
Highly dependent on capability of mind to generalize.
Takes place at different levels of complexity.
This presentation is about the raster and vector data in GIS which is important and costly as well, through the presentation we will learn about both type of data.
hyperspectral remote sensing and its geological applicationsabhijeet_banerjee
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Role of electromagnetic Radiation in Remote Sensing
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RS & GIS, Image Interpretation, Methods of Image interpretation, Types of interpretation, Factors governing image interpretation, Activities to interpret image, Sensors, Role of sensors in Image derivation, Aerial Photography, LISS-3, Image characteristics, Special characteristics, Shadow, Texture, Pattern, associated features in images
this presentation briefly describes the digital image processing and its various procedures and techniques which include image correction or rectification with remote sensing data/ images. it also contains various image classification techniques.
IMAGE INTERPRETATION
Act of examining images to identify objects and judge their significance.
Information extraction process from the images.
An interpreter is a specialist trained in study of photography or imagery, in addition to his own discipline.
Aerial photographs and remote Sensing images employ electro magnetic energy as the mean of detecting and measuring target characteristics.
Involves a considerable amount of subjective judgment.
Highly dependent on capability of mind to generalize.
Takes place at different levels of complexity.
This presentation is about the raster and vector data in GIS which is important and costly as well, through the presentation we will learn about both type of data.
hyperspectral remote sensing and its geological applicationsabhijeet_banerjee
this is an introductory presentation on hyperspectral remote sensing, which essential deals with the distinguishing features, imaging spectrometers and its types, and some of the geological applications of hyperspectral remote sensing.
Role of electromagnetic Radiation in Remote SensingNzar Braim
Role of electromagnetic Radiation in Remote Sensing
It should be clear by now that the electromagnetic waves are originator and
carrier of information in Earth observation. The information content of the products delivered by a given type of sensor is essentially related to the parameters, mainly frequency (or wavelength) and polarization, characterizing the observing system, including the geometry at which data are acquired. Therefore, the specifications of an EO system, which include the type of sensor, the band of operation, the observation angle, etc.
RS & GIS, Image Interpretation, Methods of Image interpretation, Types of interpretation, Factors governing image interpretation, Activities to interpret image, Sensors, Role of sensors in Image derivation, Aerial Photography, LISS-3, Image characteristics, Special characteristics, Shadow, Texture, Pattern, associated features in images
this presentation briefly describes the digital image processing and its various procedures and techniques which include image correction or rectification with remote sensing data/ images. it also contains various image classification techniques.
Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor.
Image reconstruction in CT is mostly a mathematical process however, this presentation tries to explain the complicated process of image reconstruction in a visual way, mainly focusing om Filtered back projection, Iterative Reconstruction and AI based image reconstruction.
Digital Image Processing is the manipulation of the digital data with the help of the computer hardware and software to produce digital maps in which specific information has been extracted and highlighted. Visual image interpretation techniques have certain disadvantages and may require extensive training and are labor intensive.
In this technique, the spectral characteristics are not always fully evaluated because of the limited ability of the eye to discern tonal value and analyze the spectral changes.
If the data are in digital mode, the remote sensing data can be analyzed using digital image processing techniques and such a data base can be used in Raster GIS.
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Satellite remote sensing data in general and digital data in particular have been used as basic inputs for the inventory and mapping of natural resources of the earth surface like forestry, soils, geology and agriculture.
Space borne remote sensing data suffer from a variety of radiometric, atmospheric and geometric errors, earth‟s rotation and so on.
These distortions would diminish the accuracy of the information extracted and reduce the utility of the data. So these errors required to be corrected.
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2. What is a Digital Image?
Most remote sensing data can be
represented in 2 interchangeable forms:
Photograph-like imagery
Arrays of digital brightness values
3.
4.
5.
6.
7.
8.
9.
10. Colour Composite Displays
We typically create multispectral image
displays or colour composite images by
showing different image bands in varying
display combinations.
19. General Appearance of Surface Features on
Colour Composite Images
Feature
True Colour
False
Colour
trees and bushes
olive green
red
crops
medium to light green
pink to red
wetland vegetation dark green to black
dark red
water
shades of blue and green blue to black
urban areas
white to light blue
blue to grey
bare soil
white to light grey
blue to grey
Source: U.S. Department of Defense, 1995. Multispectral Users Guide.
21. Image Preprocessing
Operations aim to correct distorted or
degraded image data to create a more
faithful representation of the original
scene.
"rectification and restoration"
spatial filtering
radiometric restoration (destriping)
geometric correction
22. Preprocessing functions involve those operations that are
normally required prior to the main data analysis and
extraction of information, and are generally grouped as
radiometric corrections
geometric corrections.
Radiometric corrections include correcting the data for
sensor irregularities and unwanted sensor or atmospheric
noise, and converting the data so they accurately represent
the reflected or emitted radiation measured by the sensor.
Geometric corrections include correcting for geometric
distortions due to sensor-Earth geometry variations, and
conversion of the data to real world coordinates (e.g. latitude
and longitude) on the Earth's surface.
23. Various methods of atmospheric correction can be applied ranging
from detailed modeling of the atmospheric conditions during data
acquisition, to simple calculations based solely on the image data.
An example of the latter method is to examine the observed
brightness values (digital numbers), in an area of shadow or for a
very dark object (such as a large clear lake - A) and determine the
minimum value (B). The correction is applied by subtracting the
minimum observed value, determined for each specific band, from
all pixel values in each respective band.
24. Noise in an image may be due to irregularities or errors that
occur in the sensor response and/or data recording and
transmission. Common forms of noise include systematic
striping or banding and dropped lines.
Both of these effects should be corrected before further
enhancement or classification is performed.
25. Image Registration (Geo-referencing)
Registration is the process of superimposing an
image over a map or over another already
registered data. The method of image
registration or “geo-referencing” can be
divided into two types: “image-to-imageregistration” and “image-to-map-registration”.
Selected image data of the Khorat area was
rectified with reference to the 1:50 000 scale
topographic maps (image-to-map-registration).
image-to-map-registration
Further imagery was geo-referenced to this
already registered satellite image using the
image-to-image registration.
26.
27. The geometric registration process 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).
The true ground coordinates are typically measured from a map
(B - B1 to B4), either in paper or digital format. This is image-to-map
registration.
28. Geometric registration may also be performed by registering one (or more)
images to another image, instead of to geographic coordinates. This is called
image-to-image registration and is often done prior to performing various
image transformation procedures,
In order to actually geometrically correct the original distorted image, a
procedure called resampling is used to determine the digital values to
place in the new pixel locations of the corrected output image.
3 common methods for resampling:
Nearest neighbour,
Bilinear interpolation,
Cubic convolution.
Nearest neighbour resampling uses the
digital value from the pixel in the original
image which is nearest to the new pixel
location in the corrected image.
This is the simplest method and does
not alter the original values, but may
result in some pixel values being
duplicated while others are lost. This
method also tends to result in a
disjointed or blocky image appearance.
29. Bilinear interpolation
resampling takes a
weighted average of four
pixels in the original image
nearest to the new pixel
location. The averaging
process alters the original
pixel values and creates
entirely new digital values in
the output image.
This may be undesirable if further processing and analysis,
such as classification based on spectral response, is to be
done. If this is the case, resampling may best be done after the
classification process.
30. Cubic convolution
resampling goes even further
to calculate a distance
weighted average of a block
of sixteen pixels from the
original image which
surround the new output
pixel location. As with
bilinear interpolation, this
method results in completely
new pixel values.
However, these two methods both produce images which have
a much sharper appearance and avoid the blocky appearance
of the nearest neighbour method.
31. Spatial filtering
• Spatial information
– Things close together more alike than things further apart
(spatial auto-correlation)
– Many features of interest have spatial structure such as
edges, shapes, patterns (roads, rivers, coastlines,
irrigation patterns etc. etc.)
• Spatial filters divided into two broad categories
– Feature detection e.g. edges
– Image enhancement e.g. smoothing “speckly” data e.g.
RADAR
31
33. How do we exploit this?
• Spatial filters highlight or suppress specific
features based on spatial frequency
– Related to texture – rapid changes of DN value =
“rough”, slow changes (or none) = “smooth”
43
49
48
49
51
43
50
65
54
51
12
14
9
9
10
43
49
48
49
51
210
225
199
188
Smooth(ish)
189
Rough(ish)
Darker, horizontal
linear feature
Bright, horizontal
linear feature
33
34. Convolution (spatial) filtering
• Construct a “kernel” window (3x3, 5x5, 7x7 etc.) to
enhances/remove these spatial feature
• Compute weighted average of pixels in moving window,
and assigning that average value to centre pixel.
• choice of weights determines how filter affects image
34
35. Convolution (spatial) filtering
• Filter moves over all pixels in input, calculate value
of central pixel each time e.g.
43
49
48
49
51
43
50
65
54
51
12
14
9
9
10
43
49
48
49
51
210
225
199
188
189
Input image
??
1/9
1/9
1/9
1/9
1/9
1/9
??
1/9
1/9
??
1/9
filter
Output image
35
36. Convolution (spatial) filtering
• For first pixel in output image
– Output DN = 1/9*43 + 1/9*49 + 1/9*48 + 1/9*43 + 1/9*50 +
1/9*65 + 1/9*12 + 1/9*14 + 1/9*9 = 37
– Then move filter one place to right (blue square) and do same
again so output DN = 1/9*(49+48+49+50+65+54+14+9+9) =
38.6
– And again….. DN = 1/9*(48+49+51+65+54+51+9+9+10) = 38.4
• This is mean filter
• Acts to “smooth” or blur image
43
49
48
49
51
43
50
65
54
14
9
9
49
48
49
51
210
225
199
188
189
38.4
10
43
38.6
51
12
37
Output image
36
37. Convolution (spatial) filtering
• Mean filter known as low-pass filter i.e. allows low frequency
information to pass through but smooths out higher
frequency (rapidly changing DN values)
– Used to remove high frequency “speckle” from data
• Opposite is high-pass filter
– Used to enhance high frequency information such as
lines and point features while getting rid of low frequency
information
High pass
37
38. Convolution (spatial) filtering
• Can also have directional filters
– Used to enhance edge information in a given direction
– Special case of high-pass filter
Vertical edge
enhancement filter
Horizontal edge
enhancement filter
38
39. Practical
• Try out various filters of various sizes
• See what effect each has, and construct your
own filters
– High-pass filters used for edge detection
• Often used in machine vision applications (e.g. robotics
and/or industrial applications)
– Directional high-pass filters used to detect
edges of specific orientation
– Low-pass filters used to suppress high freq.
information e.g. to remove “speckle”
39