Digital image processing

746A27 Remote Sensing and GIS 746A27 Remote Sensing and GIS

Lecture – 4

Digital image processing

Chandan Roy Guest Lecturer

Department of Computer and Information Science

Linköping University

Linköping University

Digital Image Processing

Most of the common image processing functions available Most of the common image processing functions available in image analysis systems can be categorized into the

following four categories

• Preprocessing

• Image Enhancement

• Image Transformation

• Image Transformation

• Image Classification and Analysis

Preprocessing

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 or geometric corrections.

Pre-processing operations, sometimes referred to as

image restoration and rectification, are intended to

correct for sensor- and platform-specific radiometric

correct for sensor and platform specific radiometric

and geometric distortions of data.

St i i th i

Strip in the image

Dropping of line or

signal missing

Mosaic

MOSAIC creates a new image by spatially orienting g y p y g

overlapping images and optionally balancing the numeric characteristics of the image set based on the overlapping areas.

areas.

Input images must have the same data type, resolution and reference system.

Removing the strip

DESTRIPE removes the striping caused by variable detector output in scanner imagery

output in scanner imagery.

Radiance

RADIANCE converts raw satellite Dn values to calibrated RADIANCE converts raw satellite Dn values to calibrated radiances using lookup tables of gain and offset setting for LANDSAT satellites 1-5 and user-defined values for

Lmin/Lmax or Offset/Gain for other sensor systems Lmin/Lmax or Offset/Gain for other sensor systems.

Conversion to radiances is used to facilitate comparisons

between images from different dates.

Atmospheric correction t osp e c co ect o

Here atmospheric effects are removed through different calculations.

Four models are generally used for atmospheric correction:

Four models are generally used for atmospheric correction:

• Dark Object Subtraction (DOS) model,

• Chavez's Cos(t) model

• Full radiative transfer equation model (FULL) and Full radiative transfer equation model (FULL), and

• Apparent Reflectance Model (ARM)

In each case the input consists of a raw image band and a In each case, the input consists of a raw image band and a set of atmospheric and viewing condition parameters. The output in each case is an image of proportional

reflectances expressed in real number format as a value

reflectances, expressed in real number format as a value

from 0.0 to 1.0.

Enhancement

Satellite images are enhanced for easy visual

interpretation and understanding of imagery. Although radiometric corrections for illumination, atmospheric , p

influences, and sensor characteristics may be done prior to distribution of data to the user, the image may still not be optimized for visual interpretation. p p

Image stretching

In raw imagery, the useful data often populates only In raw imagery, the useful data often populates only a small portion of the available range of digital

values. This creates a lots of problem during

classification To overcome this problem image

classification. To overcome this problem image

stretching is used.

Stretching Stretching

Before

After

After

Image composition

f

Composite produces a 24-bit color composite image from three bands of byte binary imagery for display and visual analysis. In composite images land use types can be

differentiated easily than a single band.

23

123 234

241 241

321

432

Making composite images in different sequences generates diff t lt Thi l i ti d t

different results. This color variation occur due to

interaction of the particular bands with the ground surface

objects and placement of the bands during composition.

Spatial filtering

encompasses another set of digital processing functions encompasses another set of digital processing functions which are used to enhance the appearance of an image.

Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency

features in an image based on their spatial frequency.

B a ing the calc lation pe fo med and the eightings By varying the calculation performed and the weightings of the individual pixels in the filter window, filters can be designed to enhance or suppress different types of

f t

features.

Low-pass filter

A low-pass filter is designed to emphasize larger A low pass filter is designed to emphasize larger,

homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image

smooth the appearance of an image.

High-pass filter

High pass filters do the opposite and serve to sharpen the High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image.

Directional or edge detection filters Directional, or edge detection filters

Directional, or edge detection filters are designed to

highlight linear features, such as roads or field boundaries.

Th filt l b d i d t h f t

These filters can also be designed to enhance features which are oriented in specific directions. These filters are useful in applications such as geology, for the detection of li l i t t

linear geologic structures.

Before filtering After filtering

Pansharpen

Pansharpen uses a high-resolution panchromatic image to increase the spatial resolution of low-resolution

multispectral images.

Image Transformations

Image transformations typically involve the Image transformations typically involve the

manipulation of multiple bands of data, whether from a single multispectral image or from two or more images of the same area acquired at different times

of the same area acquired at different times.

Basic image transformations apply simple

arithmetic operations to the image data Image arithmetic operations to the image data. Image subtraction is often used to identify changes that have occurred between images collected on

different dates.

1

image

2

image g

Among other image

transformation techniques Spatial ratioing, NDVI, PCA are commonly used.

Resultant

image

Image Classification

C t f i l ifi ti

Concept of image classification

Objectives of classification j

RS image Classification g

Image classification in remote sensing

Image classification in remote sensing

Multi layer perceptron classifier Multi layer perceptron classifier

MLP undertakes the classification of remotely sensed

imagery through a Multi-Layer Perceptron neural network f

classifier using the back propagation (BP) algorithm. The calculation is based on information from training sites.

The user must first specify the classification option, whether to train the network or use existing weights.

Kohonen’s Self-Organizing Map (SOM)

SOM undertakes both unsupervised and supervised

classification of remotely sensed imagery using Kohonen’s

Self-Organizing Map (SOM) neural network g g