Most of the common image processing functions available in image analysis systems can be categorized into the following four categories:

1. Preprocessing (Image rectification and restoration)

2. Image Enhancement

3. Image Classification and Analysis

4. Data Merging and GIS Interpretation

It is the task of processing and analyzing the digital data using some image processing algorithm. The analysis of relies only upon multispectral characteristic of the feature represented in the form of tone and color.

Digital image processing (DIP)

UNIT III

Techniques of Digital Image ProcessingInitial Data Statistics

Statistical information such as minimum and maximum values of the data set, mean, standard deviation, and variance for each band are calculated. Histograms and scatter-grams provide a graphical view of the nature of different bands.

Image Rectification and Restoration ( or Preprocessing)

These are correction needed for the distortion or degradations of raw data. Radiometric and geometric correction are applicable to this.

Image Enhancement

Purpose of this is to improve the appearance of the imaginary and to assist in subsequent visual interpretation and analysis. Normally, image enhancement involves techniques for increasing the visual distinction between features by improving tonal distinction between various features in a sene using technique of contrast stretching.

Image Transformation

These are operations similar in concept to image enhancement. Generally, image enhancement operation is carried out on a single band of data, while image transformations are usually on multiple bands.

Image Classification

The objective of the classification is to replace visual analysis of the image data with quantitative techniques for automating the identification of features in a scene.

Digital Data

Initial Statistic ExtractionInitial Display of Image

Ancillary Data

Image Enhancement Visual Analysis

Image Classification

Unsupervised Supervised

Classified Output

Post processing operation

Data Merging

Assessment of Accuracy

Report DataMaps and Images

Image Rectification and Restoration

Image Enhancement (Histogram Example)

Original

Histogram Example (cont. )

Poor contrast

Histogram Example (cont. )

Poor contrast

Histogram Example (cont. )

Enhanced contrast

Image HistogramAn image histogram is a

graphical representation of the

brightness values that

comprise an image. The

brightness values (i.e. 0-255)

are displayed along the x-axis

of the graph and the frequency

of occurrence of each of these

values in the image on the Y-

axis. By manipulating the

range of digital values in an

image, i.e. graphically

represented by its histogram,

various enhancement can be

applied to the data. However,

these can be grouped under

two categories:

1. Linear contrast Enhancement

2. Non linear contrast Enhancement

Spatial FilteringSpatial filtering is the digital processing function that are used to enhance the appearance of an image. Spatial features are designed to highlight or suppress specific features in an image based on their spatial frequency.

Spatial frequency is related to the concept of image texture. It refers to the frequency of the variations in tone that appear in an image. Rough texture areas of an image, where the changes in tone are abrupt over a small area, have high spatial frequencies, while smooth areas with little variation in tone over several pixels, have low spatial frequencies.

Types of Filters

1. Low-pass Filter: is designed to emphasize large homogenous areas of similar tone and reduce the smaller detail in an image. Thus, these filters generally serve to smooth the appearance of an image.

2. High-pass Filter: such filters do the opposite job as low-pass filter. They are served to sharpen the appearance of fine details in an image.

Other Filters

1. High boost filters

2. Directional or edge detection filters

Smoothing and Sharpening Examples

Smoothing(Low-pass Filter)

Sharpening(High-pass Filter)

Image Classification

• To identify and map areas with similar characteristics

• To assign meaningful categories such as land-use or land-cover classes to pixel values

Purpose

1. Supervised classification

2. Unsupervised classification

Classification Methods

Supervised Classification

In this classification method, an analyst identifies the imaginary in terms of homogenous representative samples of different surface cover type of interest. These samples are called as “Training Areas”.

The selection of appropriate training area is based on the analyst’s familiarity with geographical area and knowledge of the actual surface cover types present in the image.

The numerical information in all spectral bands for the pixels comprising these areas are used to train the computer to recognize specially similar areas for each class.

Therefore, in supervised classification, the analyst is first identifies the information classes based on which it determines the spectral classes which represent them.

Common Classifiers: 1. Parallel-piped classifiers

2. Minimum distance to mean classifiers

3. Maximum likelihood classifiers (MLC)

Digital Image

Supervised Classification

The computer then creates...

Supervised classification requires the analyst to select training areas where he/she knows what is on the ground and then digitize a polygon within that area…

Mean Spectral Signatures

Known Conifer Area

Known Water Areac

Known Deciduous Area

Conifer

Deciduous

Water

Supervised Classification

Multispectral ImageInformation

(Classified Image)

Mean Spectral Signatures

Spectral Signature of Next Pixel to be Classified

Conifer

Deciduous

Water Unknown

Unsupervised Classification

Unsupervised classification reverses the supervised classification process. Spectral classes are grouped first, based only on the numerical information in the data and are then matched by the analyst to information classes.

Programs called Clustering algorithms are used to determine the natural groupings or structures in the data. Usually, the analyst specify how many groups or clusters are to be looked for in the data.

In addition to specifying the desired number of classes, the analyst may also specify the parameters related to separation distance among the clusters and variation with each cluster

However, algorithm for this classification operates in a two- pass mode. In the first pass, the algorithm sequentially builds class clusters. In second pass, a minimum distance classifier is applied to the whole data set on a pixel-by-pixel basis, where each pixel is assigned to one of the mean vectors created in pass 1 mode.

Unsupervised Classification

Digital Image

The analyst requests the computer to examine the image and extract a number of spectrally distinct clusters… Spectrally Distinct Clusters

Cluster 3

Cluster 5

Cluster 1

Cluster 6

Cluster 2

Cluster 4

Saved Clusters

Cluster 3

Cluster 5

Cluster 1

Cluster 6

Cluster 2

Cluster 4

Unsupervised Classification

Output Classified Image

Unknown

Next Pixel to be Classified

Unsupervised Classification

??? Water

??? Water

??? Conifer

??? Conifer

??? Hardwood

??? Hardwood

The result of the unsupervised classification is not yet information until…

The analyst determines the ground cover for each of the clusters…

Remote Sensing Applications

Forestry & Ecosystem1. Forest cover & density mapping

2. Deforestation mapping

3. Forest fire mapping

4. Wetland mapping and monitoring

5. Biomass estimation

6. Species inventoryAgriculture1. Crop type classification

2. Crop condition assessment

3. Crop yield estimation

4. Mapping of soil characteristic

5. Soil moisture estimation

Land Use/Land Cover mapping1. Natural resource management

2. Wildlife protection

3. Encroachment

Urban Planning1. Land parcel mapping

2. Infrastructure mapping

3. Land use change detection

4. Future urban expansion planning

Ocean applications1. Storm forecasting

2. Water quality monitoring

3. Aquaculture inventory and monitoring

4. Navigation routing

5. Coastal vegetation mapping

6. Oil spill

Hydrology1. Watershed mapping & management

2. Flood delineation and mapping

3. Ground water targeting

Remote Sensing Applications……cont.

Geology1. Lithological mapping

2. Mineral exploration

3. Environmental geology

4. Sedimentation mapping and monitoring

5. Geo-hazard mapping

6. Glacier mapping

Other Applications1. Flood Plain Mapping

2. Disaster Management

3. District level Planning

Land Use And Land Cover Mapping

LISS III PAN

Initial Statistics

Contrast Enhancement

Registration

Supervised Classification

Classified Image

Accuracy Assessment

Ground Data

A study on land use and land cover for a part of

Hraidwar district was carried out for the area lying

between 78007’13” E and 78016’14” E longitude and

300 N and 30008’53” N latitudes covering an area of

nearly 260 km2.

IRS-1C LISS III of April 3, 2000 was used along with

PAN image of the same date. The methodology

adopted is shown in figure.

On the basis of field visit, 11 cities

were identified. These classes are:

i). Thin forest ii). Medium forest

iii). Dense forest iv). Fallow land

v). Shrubs vi). Open land

vii). Shallow water viii). Wet land

ix). Dry sand x). Built-up-area

xi). Deep water