Basic
Digital Image Processing
Wilfredo M. Rada
Assistant Professor
University of the Philippines
Outline
I. Digital Image
II. Characteristics of a Digital Image
III. Multilayer Image
IV. Visualization
V. Three Broad Categories of Image Processing
VI. Preprocessing
VII. Contrast Enhancement
VIII.Spatial Filtering
IX. Density Slice
2
3
Digital Image
Digital image is a two-
dimensional array of pixels.
Each pixel has an
intensity value
(represented by a digital number)
and a
location address
(referenced by its row and column
numbers).
4
Sample Digital Image
5
6
Characteristics of a Digital Image
1. Spatial Resolution
2. Spectral Resolution
3. Radiometric Resolution
4. Temporal Resolution
7
Spatial Resolution
Pixel Size = 10 m
Image Width = 160 pixels
Height = 160 pixels
Pixel Size = 20 m
Image Width = 80 pixels
Height = 80 pixels
• Spatial Resolution refers to the size of the smallest
object that can be resolved on the ground.
• In a digital image, the resolution is limited by the pixel size.
8
Spatial Resolution
Pixel Size = 40 m
Image Width = 40 pixels
Height = 40 pixels
Pixel Size = 80 m
Image Width = 20 pixels
Height = 20 pixels
9
Visual Effect of Spatial Resolution
10
Spectral Resolution
Spectral Resolution refers to the specific wavelength
intervals in the electromagnetic spectrum that a
sensor can record.
Pan: 450 - 900 nm
QuickBird Blue: 450 - 520 nm
Green: 520 - 600 nm
Image Bands Red: 630 - 690 nm
Near IR 760 - 900 nm
Pan: 480 - 710 nm
SPOT-5 Green: 500 - 590 nm
Red: 610 - 680 nm
Image Bands Near IR: 780 – 890 nm
ShortWave IR: 1,580 – 1,750 nm
11
Radiometric Resolution
8-bit quantization
(256 levels)
6-bit quantization
(64 levels)
• Radiometric Resolution refers to the smallest change in
intensity level that can be detected by the sensing system.
• In a digital image, the radiometric resolution is limited by the
number of discrete quantization levels used to digitize the
continuous intensity value.
12
2-bit quantization
(4 levels)
1-bit quantization
(2 levels)
4-bit quantization
(16 levels)
3-bit quantization
(8 levels)
13
Gray Scale
[256 level gray scale]
Most raw unprocessed satellite
imagery is stored in a gray scale
format.
A gray scale is a color scale that
ranges from black to white, with
varying intermediate shades of
gray.
A commonly used gray scale for
remote sensing image processing
is a 256 shade gray scale, where a
value of 0 represents a pure black
color, the value of 255 represents
pure white, and each value in
between represents a progressively
darker shade of gray.
14
Temporal Resolution
Temporal Resolution relates to the repeat cycle or interval between successive acquisitions.
Examples:
Landsat-7 Revisit Time: 15 days
SPOT-5 Revisit Time: 2-3 days depending on Latitude
IKONOS Revisit Time: Approximately 3 days at 40° latitude
QuickBird Revisit Time: 1-3.5 days depending on Latitude
(30º off-nadir)
15
Multilayer Image
Multilayer image is formed by "stacking"
images from the same area together.
Each component image is a layer in the
multilayer image and carry some specific
information about the area.
Multilayer images can also be formed by
combining images obtained from different
sensors, and other subsidiary data.
16
Multilayer Image
An illustration of a multilayer image consisting of five component layers.
17
Visualization
SUBTRACTIVE PRIMARY COLORS
18
Additive Color Display
Green + Blue
= Cyan
Red + Green
= Yellow
Red + Blue
= Magenta
Red + Green
+ Blue
= White
19
RGB Band Composite
20
Sample Landsat TM Composite Images BAND 1
BAND 2
BAND 4 BAND 6
BAND 3
BAND 5
BAND 7
RGB 741 RGB 572
RGB 432 RGB 543
21
Certain bands or band combinations are better than others for identifying specific land cover features.
Landsat TM Red= band 3, Green = band 2, Blue = band 1
Landsat TM Red= band 4, Green = band 5, Blue = band 3
22
Three Broad Categories
of Image Processing
Image Restoration (Preprocessing)
Image Enhancement
Classification and Information Extraction
23
Digital Image Processing Flow
24
Preprocessing
Preprocessing is an important and diverse set of image preparation programs that act to offset problems with the band data and recalculate DN values that minimize these problems.
Among the radiometric and geometric
corrections are:
• atmospheric correction
• sun illumination geometry
• surface-induced geometric distortions
• spacecraft velocity and attitude variations (roll, pitch, and yaw)
• effects of Earth rotation, elevation, curvature (including skew effects),
• abnormalities of instrument performance
• loss of specific scan lines (requires destriping), and others
25
26
Sample Geometric Distortions
27
Sample Geometric Distortions
28
Sun Illumination Geometry
29
Contrast Enhancement
It is an image processing procedure that improves
the contrast ratio of images.
The original narrow range of digital values is
expanded to utilize the full range of available digital
values.
It is useful to examine the image histograms before
performing any image enhancement
30
Sample Image Histograms
31
Sample Contrast Enhancement Methods
32
Image & Histogram
33
Image & Histogram
34
Sample Enhanced Images
35
Spatial Filtering
Spatial filtering explores the distribution of pixels of varying brightness over an image and, especially detects and sharpens boundary discontinuities.
These changes in scene illumination, which are typically gradual rather than abrupt, produce a relation that we express quantitatively as "spatial frequencies".
Spatial Filtering
Filters that pass high frequencies and, hence, emphasize fine detail and edges, are called highpass filters.
Lowpass filters, which suppress high frequencies, are useful in smoothing an image, and may reduce or eliminate "salt and pepper" noise.
36
37
Sample Filtered Images
high pass filter
image
low pass filter
image
contrast-stretched
image
image from
a large
convolution
window
38
Smoothing Vs Sharpening
39
Ratioing
Ratioing is an enhancement process in which the DN value of one band is divided by that of any other band in the sensor array.
Image ratioing is commonly used in vegetation studies.
The most widely used measure is a normalized difference vegetation index (NDVI) which is calculated by taking the difference in brighness values between the near IR and the red bands and dividing that difference by the sum of the same two bands.
Sample NDVI Formula
For example, using Thematic mapper data, band 4 is the near IR and band 3 is red:
NDVI = (TM4 - TM3) / (TM4 + TM3)
40
41
Sample NDVI Image
42
Density Slice
Density Slice is a straightforward form of
enhancement that results from the combining
("lumping together") of DNs of different values within
a specified range or interval into a single value.
It is also called "level slice" method and works best
on single band images. It is especially useful when a
given surface feature has a unique and generally
narrow set of DN values.
43
Sample Density Sliced Images
This map has four levels
or slices. The lavender
tends to demarcate a gray
level (DN 43 to 48) that
associates with urban
areas.
44
Six gray levels (each
representing a DN range)
have been colorized as
follows:
Black = (DN) 0-19;
Blue = 20-34;
Red = 35-44; White = 45-54;
Brown = 55-69; Green = 70+
The black pattern is almost entirely tied to water; the blue denotes
heavily built up areas; the green marks vegetation; the other colors
indicate varying degrees of suburbanization and probably some open
areas.
Sample Density Sliced Images
45