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

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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).

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Sample Digital Image

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Characteristics of a Digital Image

1. Spatial Resolution

2. Spectral Resolution

3. Radiometric Resolution

4. Temporal Resolution

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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.

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Spatial Resolution

Pixel Size = 40 m

Image Width = 40 pixels

Height = 40 pixels

Pixel Size = 80 m

Image Width = 20 pixels

Height = 20 pixels

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Visual Effect of Spatial Resolution

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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

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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.

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2-bit quantization

(4 levels)

1-bit quantization

(2 levels)

4-bit quantization

(16 levels)

3-bit quantization

(8 levels)

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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.

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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)

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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.

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Multilayer Image

An illustration of a multilayer image consisting of five component layers.

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Visualization

SUBTRACTIVE PRIMARY COLORS

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Additive Color Display

Green + Blue

= Cyan

Red + Green

= Yellow

Red + Blue

= Magenta

Red + Green

+ Blue

= White

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RGB Band Composite

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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

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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

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Three Broad Categories

of Image Processing

Image Restoration (Preprocessing)

Image Enhancement

Classification and Information Extraction

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Digital Image Processing Flow

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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

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Sample Geometric Distortions

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Sample Geometric Distortions

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Sun Illumination Geometry

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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

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Sample Image Histograms

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Sample Contrast Enhancement Methods

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Image & Histogram

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Image & Histogram

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Sample Enhanced Images

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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.

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Sample Filtered Images

high pass filter

image

low pass filter

image

contrast-stretched

image

image from

a large

convolution

window

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Smoothing Vs Sharpening

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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)

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Sample NDVI Image

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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.

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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.

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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

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