1

CHAPTER 1

INTRODUCTION

1.1 GENERAL

Preparation of maps has been one of the challenging areas in surveying. The

conventional methods are to go to area directly and take measurements, level etc and

plot the map. This requires large human resources and consumes time and money.

Also accuracy of this map is susceptible to human and many instruments involved.

But in the present days we cannot afford time. Also Map revision is traditionally a

manual task especially when maps are updated on the basis of aerial images and

existing map data. For this reason, maps are typically out of date. For example, it has

been reported that, for a number of reasons, the revision lag-time for topographic

maps from the United States Geological Survey (USGS) is more than 23 years. To

overcome these types of difficulties methods of extraction of road network from

satellite image plays an important role.

The different methods of road extraction from satellite image includes the

Automatic method (J. B. Mena 2005) and Semi-automatic method (Jun Zhou 2006) of

road extraction. In automatic method the human computer interaction is very low or it

is said to be done with pure mathematical algorithms by the computer, whereas in the

semi-automatic method there is human interaction also along with the help of

computer. In semiautomatic road extraction, a road in the image is delineated using its

geometric and photometric properties with the initial positions provided by an

operator.

While microcomputer made their first appearance three decades ago, it is only

in the last 15 years they have become "seriously useable" machines. This situation has

occurred as the consequence of a series of developments which includes: faster

processing facility, large capacity, high performance and relatively inexpensive hard

discs; high resolution colour monitors; CD-ROM players becoming near universal;

and availability of inexpensive, high quality colour output devices and colour

scanners. These hardware technology changes have gone in parallel with changes in

better data conversion, software for scanners, better software for image manipulation

and storage, and improvements in database management system.

2

The innovation and development in computer, communication and software is

contributing towards the growth of information technology. The net result of these

changes is that it is now relatively easy to create, store, retrieve, and analyze large

quantities of spatial and non-spatial data of urban and transportation system. A related

change is the rapid development of spatial information technologies such as Remote

Sensing (RS), Global Positioning System (GPS) and Geographical Information

System (GIS).This made the process like road network generation, map revision,

flood mapping, urban change detection, etc. easy as compared to the conventional

methods to the same.

Road tracking methods make assumptions about road characteristics like, roads are

elongated, road surfaces are usually homogeneous, there is adequate contrast between

road and adjacent areas, roads may not be elongated at crossings, bridges, and ramps,

road surfaces may be built from various materials that cause radiometric changes,

ground objects such as trees, houses, vehicles and shadows may occlude the road

surface and may strongly influence the road appearance, road surfaces may not have

adequate contrast with adjacent areas because of road texture, lighting conditions, and

weather conditions, the resolution of satellite images can have a significant impact on

computer vision algorithms.

One problem with these systems is that such assumptions are pre-defined and

fixed whereas image road features vary considerably. Such properties cannot be

completely predicted and they constitute the main source of problems with fully

automated systems. One solution to this problem is to adopt a semiautomatic

approach that retains the ‘‘the human in the loop” where computer vision algorithms

are used to assist humans performing these tasks.

The report briefly explains the preparation of road from satellite imagery by

the semi automatic method which includes the process like geo-referencing,

mosaicing, haze reduction, noise removal, image enhancements like contrast

stretching, filtering, and edge enhancement by using software ERDAS Imagine and

extraction of selected area and digitizing done in Arc GIS. Also uses EDM for width

measurement of roads at junctions and handheld GPS for non visible roads in the

satellite image.

3

1.2 OBJECTIVES

The main objectives of the project is extraction of roads from satellite images of the

selected 16 wards of Thiruvananthapuram Corporation, preparation of road network

which include digitization of road network using GIS, identification of missing road

using hand held GPS and road width measurement using EDM.

4

CHAPTER 2

LITERATURE REVIEW

2.1 GENERAL

The Road extraction from remotely sensed imagery has been an active

research area in map preparation for over two decades. During the past 20 years, a

number of semi-automatic and automatic methods and algorithms for road extraction

have been developed. Conventional methods of road extraction usually consist of

three main steps, road finding, road tracking, and road linking. In road finding, local

properties of the image are tested and road candidates are found using certain criteria.

The detected road candidates are then traced to form road segments. The separated

road segments are finally linked to generate a road network using geometric

constraints. In semiautomatic road extraction, a road in the image is delineated using

its geometric and photometric properties with the initial positions provided by an

operator. These methods use local geometric constraints for road tracking and linking.

Because the global structure of the road network is not considered, wrong segments

are unavoidable, and occlusions such as trees, shadows, surface anomalies, and road

width change can cause the tracking to be lost.

2.2 REMOTE SENSING

Remote sensing is the science and to some extent art of obtaining information

about an object, area, or phenomenon through the analysis of data acquired by a

device that is not in contact with the object, area, or phenomenon under

investigations. This is done by sensing and recording the reflected or emitted energy

and processing, analyzing, and applying that information. The advent of Remote

Sensing through space borne and air-borne platforms and sensors has opened new

vistas for modern, scientific surveying of earth’s natural resources. Remote sensing

data is the name given to any data where information about a location is collected

remotely, i.e. from a different location, such as collecting information about the

ground surface from inside an aircraft.

5

2.3 ERDAS Imagine 8.6

ERDAS IMAGINE is an image processing software with raster graphics editor

capabilities designed by ERDAS, Inc. for geospatial applications. ERDAS IMAGINE

is aimed primarily at geospatial raster data processing and allows the user to prepare,

display and enhance digital images for mapping use in GIS or in C ADD software. It

is a toolbox allowing the user to perform numerous operations on an image and

generate an answer to specific geographical questions. By manipulating imagery data

values and positions, it is possible to see features that would not normally be visible

and to locate geo-positions of features that would otherwise be graphical. The level of

brightness or reflectance of light from the surfaces in the image can be helpful with

vegetation analysis, prospecting for minerals etc. Other usage examples include linear

feature extraction, generation of processing work flows ("spatial models" in ERDAS

IMAGINE), import/export of data for a wide variety of formats, ortho-rectification,

mosaicing of imagery, stereo and automatic feature extraction of map data from

imagery.

The digital Image Processing done in ERDAS includes:

• Preprocessing

Geometric correction

Radiometric correction

Haze reduction

Noise removal

• Image enhancement

Contrast stretching

Filtering

Edge enhancement

2.4 ArcGIS

In the highly dynamic and complex world 'information' has become a critical

resource for effective and efficient management of organisation. Information

Technology in its various forms is enabling organizations to churn raw data into

meaningful information for effective decision making. One such form of Information

Technology (IT) is Geographic Information System (GIS). It is described as: “An

organized collection of computer hardware, software, geographic data and personnel

6

designed to efficiently capture, store, update, manipulate, analyze, and display all

forms of geographically referenced information”. According to this definition, GIS

includes not only computing capability and data, but also manages the users, and

organizations within which they function and institutional relationships that govern

their management and use of information. GIS system design and implementation

planning are not a separate process. They must occur in conjunctions with one

another.

ArcGIS is a suite consisting of a group of geographic information system

(GIS) software products produced by Esri.

ArcGIS is a system for working with maps and geographic information. It is

used for: creating and using maps; compiling geographic data; analyzing mapped

information; sharing and discovering geographic information; using maps and

geographic information in a range of applications; and managing geographic

information in a database.

The system provides an infrastructure for making maps and geographic

information available throughout an organization, across a community, and openly on

the Web.

7

2.4.1 Conceptualization of GIS

Conceptually, a GIS can be envisioned as a stacked set of map layers, where

each layer is aligned or registered to all other layers. Typically, each layer will contain

a unique geographic theme or data type. The GIS database stores both the spatial data

(where something occurs) and the attribute data (characteristics of the spatial data) for

all of the features shown on each layer. These themes may include, for example,

topography, soils, land-use, cadastral (land ownership) information, or infrastructure

such as roads, Traffic Analysis Zones (TAZ), pipelines, power lines, or sewer

networks. Figure 1 gives a schematic view of geographic layer system in GIS. By

sharing mutual geography, all layers in the GIS can be combined or overlaid in any

user-specified combination.

Fig. 1 Mapping layers of GIS

2.5 GLOBAL POSITIONING SYSTEM (GPS)

Global Positioning System (GPS) has tremendous potential for better transport

management/planning. Traffic management, emergency services (fire service,

accident relief, ambulance service, policing, etc.), are the few areas where GPS can

8

play significant role due to its capability to provide near accurate location (latitude,

longitude, altitude) and other details. Traffic routing, movement of vehicles, VIP

movement, taxi service, fleet management for passenger and cargo services etc.

becomes easier by using GPS receivers on vehicles. Use of GPS along with GIS

database of the city can help to perform the above tasks more effectively. GPS is also

very useful in creating accurate spatial databases. Global positioning system is an

earth-orbiting Satellite based system that provides signals anywhere on or above

earth, 24 hours a day, round the year, and irrespective of weather, and that can be used

to determine precise time and the position of a GPS receiver in three dimensions. This

technology is increasingly used as input for GIS particularly for precise positioning of

geo-spatial data and for collection of data from the field. One major advantage is its

capability of forming a powerful building block in an integrated system. GPS together

with a co-ordinate system and GIS produces a map and the map facilitates navigation.

GPS is rapidly becoming an important tool to the GIS and Remote sensing industries.

2.5.1 Concept of GPS

GPS consists of a constellation of radio navigation satellite and a ground

control segment. It manages satellite operation and users with specialized receivers

who use the satellite data to satisfy a broad range of positioning requirements. In

brief, following are the key features of GPS:-

1. The basis of GPS is ‘triangulation’ more precisely trilateration from satellites

2. A GPS receiver measures distance using the travel time of radio signals.

3. To measure travel time GPS needs very accurate timing that is achieved with some

techniques.

4. Along with distance, one needs to know exactly where the satellites are in space.

5. Finally one must correct for any delays, the signal experience as it travels through

the atmosphere.

The whole idea behind GPS is to use satellites in space as reference points for

location here on earth. By very accurately measuring the distances from at least three

satellites, we can ‘triangulate’ our position anywhere on the earth by resection

method.

9

CHAPTER 3

METHODOLOGY

3.1 GENERAL

Methodology used for linear feature extraction can be used for the extraction

of road network from satellite imaginary. Suitable area can be selected from

Thiruvananthapuram Corporation and high resolution Cartosat image can be

collected. A shapefile of the study area will be created using ArcGIS and the image

can be cut using the shapefile. The image can be processed according to the algorithm

using ERDAS IMAGINE software. The processed image is digitized in ArcGIS and a

map of road network can be prepared.

3.2 SELECTION OF AREA

Suitable area for the project is selected from the Thiruvananthapuram

corporation map considering the availability of high resolution satellite image

(Cartosat image, 2.5m). 16 wards were selected.

Fig. 2 Ward Map of Trivandrum Corporation

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3.3 DATA AND MATERIALS

The data collection includes the collection of satellite images having sufficient

spatial resolution, GPS data using hand held GPS for jointing the missing links of

roads with the road network extracted from satellite imagery, width of road at

junctions using EDM. The processing of the data for the extraction of road network

from satellite imagery and further corrections using the additional data collected by

GPS and EDM requires software like ERDAS IMAGINE and ArcGIS.

SATELLITE IMAGES

The road map preparation requires high resolution satellite images otherwise

the roads may not be visible in the processed image. In the Department of Civil

Engineering the available images were (a) IRS LISS III which is having a spatial

resolution of 23m, (b) Panchromatic which is having a spatial resolution of 5.8m, (c)

Cartosat which is having a special resolution of 2.5m, (d) LISS IV which is having a

spatial resolution of 5.8m. Cartosat images containing the selected area were collected

from Department of Civil Engineering as it is having a high spatial resolution of 2.5m

comparing to the available satellite images.

GPS

Roads having smaller width are not able to digitize in ArcGIS. Those roads

can be plotted using hand held GPS. The hand held GPS (Magellan) having an

accuracy of 5m is collected from the Department Of Civil Engineering.

EDM

The edges of the roads having low width cannot be visible from the satellite image,

the width of the roads are to be measured and an average value is to be assigned at the

junctions to each roads meeting at the junction these are to be measured by using

EDM.

ERDAS IMAGINE

The pre processing operations like geo-referencing, mosaicing, haze reduction, noise

removal, image enhancements like contrast stretching, filtering, and edge

enhancement are to be done in ERDAS IMAGINE software.

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ArcGIS

The processes like extraction of the selected 16 wards from the satellite image and the

digitising of the extracted roads are to be done in ArcGIS software.

3.4 EXTRACTION OF ROAD

The road networks may not be clearly visible in the satellite image in the raw form

thus it is to be processed and enhanced to get the road network clearly. This include

the Pre-processing like Geo-referencing, Mosaicing of image, Shapefile preparation,

Extraction of selected area, Haze Reduction, Noise Removal and also Image

Enhancement like Contrast Stretching, Filtering and Edge Enhancement.

3.4.1 Pre-processing of images

Pre-processing refers to the image rectification and restoration procedures.

This is the initial step done in data processing.

In their raw form, as received from imaging sensors mounted on satellite

platforms, remotely-sensed data generally contain flaws or deficiencies. The

correction of deficiencies and the removal of flaws present in the data are termed pre-

processing because, quite logically, such operations are carried out before the data are

used for a particular purpose. Despite the fact that some corrections are carried out at

the ground receiving station, there is often still a need on the user’s part for some

further pre-processing. The subject is thus considered here before methods of image

enhancement and analysis are examined. It is difficult to decide what should be

included under the heading of ‘pre-processing’, since the definition of what is, or is

not, a deficiency in the data depends to a considerable extent on the use to which

those data are to be put. If, for instance, a detailed map of the distribution of particular

vegetation types is required then the geometrical distortion present in an uncorrected

remotely-sensed image will be considered to be a significant deficiency. On the other

hand, if the purpose of the study is to establish the presence or absence of a particular

class of land use (such as irrigated areas in an arid region) then a visual analysis of a

suitably-processed false-colour image will suffice and, because the study is concerned

with determining the presence or absence of a particular land use type rather than its

precise location, the geometrical distortions in the image will be seen as being of

secondary importance. A second example will show the nature of the problem .An

attempt to estimate reflectance of a specific target from remotely-sensed data will be

12

hindered, if not completely prevented, by the effects of interactions between the

incoming and outgoing electromagnetic radiation and the constituents of the

atmosphere. Correction of the imagery for atmospheric effects will, in this instance,

be considered to be an essential part of data pre-processing whereas, in some other

case (for example, discrimination between land-cover types in an area at a particular

point in time), the investigator will be interested in relative, rather than absolute, pixel

values and thus atmospheric correction would be unnecessary. Measurements of

change over time using multi-temporal image sets will, in the case of optical imagery,

require correction for atmospheric variability, and it will also be necessary to register

the images forming the multi-temporal sequence to a common geographical

coordinate system. In addition, corrections for changes in sensor calibrations will be

needed to ensure that like is compared with like.

Because of the difficulty of deciding what should be included under the

heading of pre-processing methods, an arbitrary choice has been made. Correction for

geometric, radiometric and atmospheric deficiencies, and the removal of data errors or

flaws, is covered here despite the fact that not all of these operations will necessarily

be applied in all cases. This point should be borne in mind by the reader. It should not

be assumed that the list of topics covered in this topic constitutes a menu to be

followed in each and every application. The pre-processing techniques discussed in

the following sections should, rather, be seen as being applicable in certain

circumstances and in particular cases. The investigator should decide which pre-

processing techniques are relevant on the basis of the nature of the information to be

extracted from the remotely-sensed data.

The pre-processing procedure is done as follows:

Geo-referencing:

Geo-referencing is the process of aligning spatial data (layers that are shape

files: polygons, points, etc.) to an image file such as an historical map, satellite image,

or aerial photograph. Toposheet of the study area is to be geo-referenced adopting

Projected Coordinate System, UTM, Zone 43N. With respect to the geo-referenced

toposheet the four satellite imagery are to be geo-referenced in ERDAS IMAGINE.

13

Mosaicing of image:

The selected area containing the 16 wards were distributed in two cartosat

images. The Cartosat images are to be mosaiced to make a single image. This is to be

done in ERDAS Imagine.

From data preparation menu by using mosaic tool the two geo-referenced

Cartosat images are to be mosaiced to a single image.

Shapefile preparation:

Shapefiles spatially describe geometries, points, polylines, and polygons.

These, for example, could represent water wells, rivers or road network, and lakes or

boundaries, respectively. The following procedure is done to prepare shapefile.

From the ArcCatalog a personal Geo-database was created in that a new

feature class was added with the specifications like polygonal feature, projected

coordinate system as required for the shapefile. Then using the edit tool bar the

boundary of the selected wards is traced and saved. This export to ERDAS

IMAGINE and the area is to be extracted.

Extraction of selected area:

The selected area containing the 16 wards of the Trivandrum Corporation is to

be extracted from the mosaiced Cartosat image, by preparing the shapefile of the area

in ArcGIS and cutting the area from Cartosat image in ERDAS IMAGINE.

Table 1 Selected Wards

Ward No. Ward Name Ward No. Ward Name

17 Pattom 29 Vazhuthacaud 22 Sasthamangalam 30 Kanilampara 23 Kowdiyar 43 Valyashala

24 Kuravankonam 44 Jagathy 25 Kanchankode 81 Thampanoor

26 Kununkuzhi 82 Vanchiyoor 27 Palayam 83 Sreekandeshwaram 28 Thycaud 94 Kannammoola

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Haze Reduction:

Haze compensation procedure is designed to minimize the influence of path

radiance effects. One means of haze compensation in multispectral data is to observe

the radiance recorded over target areas of essentially zero reflectance. For example,

the reflectance of deep clear water is essentially zero in the near-infrared region of the

spectrum. Therefore any signal observed over such an area represents the path

radiance, and this value can be subtracted from all pixels in the band.

Noise Removal:

Image noise is any unwanted disturbance in image data that is due to

limitation in the sensing, signal digitization or data recording process. The potential

sources of noise range from periodic drift or malfunction of a detector, to electronic

interference between sensor components to intermittent "hiccups" in the data

transmission and recording sequence. Noise can either degrade or totally mask the

true radiometric information content of a digital image. The objective of noise

removal is to restore an image close an approximation of the original scene as

possible.

3.4.2 Image Enhancement

The procedures applied to image data in order to more effectively display or

record the data for subsequent visual interpretation. Normally, image enhancement

involves techniques for increasing the visual distinctions between features in a scene.

The, objective is to create a new” images from the original image data in order to

increase the amount of information that can be visually interpreted from the data. The

enhanced images can be displayed interactively on a monitor or they can be recorded

in a hardcopy format, either in black and white or in color. There are no simple rules

for producing the single “best" image for a particular application. Often several

enhancements made from the same “raw” image are necessary.

The various image enhancements done to the imagery in ERDAS IMAGINE

includes:

15

Contrast Stretching:

Contrast stretching (often called normalization) is a simple image

enhancement technique that attempts to improve the contrast in an image by

`stretching' the range of intensity values it contains to span a desired range of values,

e.g. the full range of pixel values that the image type concerned allows. It differs from

the more sophisticated histogram equalization in that it can only apply a linear scaling

function to the image pixel values. As a result the `enhancement' is less harsh.

The intent of contrast stretching is to expand the narrow range of brightness

values typically present in an input image over a wider range of grey values. The

result is an output image that is designed to accentuate the contrast between features

of interest to the image analyst.

Contrast Stretching is to be done such that the required features will be more

clearly visible in the satellite images. The breakpoint of each band of the image is to

be adjusted in the ERDAS IMAGINE so that roads are more clearly visible. For each

band of multi-spectral images the breakpoints are to be adjusted and check whether

the roads are visible. The resultant image will give a better idea of location of roads in

the images.

Filtering:

Spatial filters emphasize or deemphasize image data of various spectral

frequencies. Spatial frequency refers to the “roughness” of the tonal variations

occurring in an image. Image areas of high spatial frequency are tonally rough. That is

gray levels in these areas change abruptly over a relatively small number of pixels

(e.g. across roads or field borders). “Smooth” image areas are those of low spatial

frequency, where gray levels vary only gradually over a relatively large number of

pixels (e.g. large agricultural fields or water bodies)

Low pass filters are designed to emphasize low frequency features (large area

changes in brightness) and deemphasize the high frequency components of an image

(local detail). A simple low pass filter may be implemented by passing a moving

window throughout an original image and creating a second image whose DN at each

pixel corresponds to the local average within the moving window at each of its

positions in the original image. Low pass filtering is done in ERDAS IMAGINE by

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passing a moving a 3x3 pixel window throughout the original image and a low

frequency image is obtained. The low frequency image obtained after low pass filter

is smooth or blurred so that the original image details are blurred.

High pass filters do just the reverse of low pass filter. They emphasize the

detailed high frequency components of an image and deemphasize the more general

low frequency information. A simple high pass filter may be implemented by

subtracting a low pass filtered image (pixel by pixel) from the original, unprocessed

image. The high frequency image obtained after high pass filtering will have a high

contrast and gives a better idea of roads. The image will be sharpened and it roads

will be more clear.

Edge Enhancement:

In Edge enhancement it enhances the edge contrast of an image. It is typically

implemented in three steps:

• A high frequency component image is produced containing the edge

information. The Kernel size used to produce this image is chosen

based on the roughness of the image. “Rough” image suggest small

filter sizes (e.g. 3x3 pixels), whereas large sizes (9x9 pixels) are used

with “smooth” images.

• All or a fraction of the gray level in each pixel of the original scene is

added back to the high frequency component image.

• The composite image is contrast stretched. This result in an image

containing local contrast enhancement of high frequency features that

also preserves the low frequency brightness information contained in

the scene.

In ERDAS IMAGINE, the high frequency image is passed through a Kernel of

size 3x3 and a high frequency image is produced containing the edge information.

The composite image is then contrast stretched. This image is a high frequency

sharpened image. The edges of roads will be clearer in these images. This image

clearly gives the details of roads in the study area for their extraction. This road

details are then digitized in Arc GIS.

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3.5 DIGITIZING OF EXTRACTED ROADS

The processed image is to be loaded in ArcGIS for the extraction of roads. The

roads are digitized by visual interpretation and saved as corresponding feature class

for each image. A road passing through an area with uniformly distributed vegetation,

like paddy field becomes prominent due to their different reflection characteristics.

The areas where there is a very good background contrast then the road section

throughout and edges of the road can be identified clearly.

3.6 PLOTTING OF MISSING ROADS USING GPS

Roads having smaller width are not able to digitize in ArcGIS. Those roads

can be plotted using hand held GPS. The readings, latitudes and longitudes, of roads

are to be taken manually by field investigation and need to be added to the missing

links manually.

3.7 ROAD WIDTH MEASUREMENT USING EDM

Generally the width of the road is same from junction to junction. Even though

there are slight variations but we are assuming it to be uniform. The widths of

extracted roads are to be measured using Electronic Distance Meter (EDM) at various

locations and the average value is assigned as the uniform value.

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CHAPTER 4

DATA PROCESSING

4.1 SATELLITE IMAGES OF STUDY AREA

Cartosat images containing the selected area (Cartosat 547354 & 547355) were collected from Geo-informatics lab as it is having a high spacial resolution of 2.5m comparing to the available satellite images.

Fig. 3 Cartosat Images (2.5m)

4.2 PRE-PROCESSING OF IMAGES

In their raw form, as received from imaging sensors mounted on satellite platforms, remotely-sensed data generally contain flaws or deficiencies. The correction of deficiencies and the removal of flaws present in the data are termed pre-processing because, quite logically, such operations are carried out before the data are used for a particular purpose.

Pre-processing refers to the image rectification and restoration procedures. This is the initial step done in data processing.

Geo-referencing:

Geo-referencing of toposheet of the study area is done and the projection system adopted is Projected Coordinate System, UTM, Zone 43N. With respect to the geo-referenced toposheet the four satellite imagery were geo-referenced in ERDAS IMAGINE.

19

Fig. 4 Geo-referenced Images

Mosaicing of image:

The selected area containing the 16 wards were distributed in two Cartosat images. The Cartosat images are mosaiced to make a single image. This is done in ERDAS Imagine.

From data preparation menu by using mosaic tool the two geo-referenced Cartosat images are mosaiced to a single image.

Fig. 5 Mosaiced Image

Shapefile preparation:

From the Arc Catalog a personal Geo-database was created in that a new feature class was added with the specifications like polygonal feature, projected coordinate system as required for the shapefile. Then using the edit tool bar the boundary of the

20

selected wards is traced and saved. This is exported to ERDAS IMAGINE and the area is extracted.

Fig. 6 Shape File of the Selected Area

Extraction of selected area:

The selected area containing the 16 wards of the Trivandrum Corporation is extracted(area 25sq km) from the mosaiced Cartosat image, by preparing the shapefile of the area in ArcGIS and cutting the area from Cartosat image in ERDAS IMAGINE.

Fig.7 Extracted Image

Haze Reduction:

Haze reduction is done in ERDAS IMAGINE. The resultant images obtained after haze reduction is shown in the fig.5. For convenience haze correction routines are

21

often applied uniformly throughout a scene. The raw image will be enhanced in contrast but the image will be blurred.

Fig. 8 Haze Reduced Image

Noise Removal:

Image noise is any unwanted disturbance in image data that is due to limitation in the sensing, signal digitization or data recording process.The objective of noise removal is to restore an image close an approximation of the original scene as possible. There was not much noise in the raw data so there was not much difference in the image obtained after noise reduction.

Fig. 9 Noise Removed Image

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4.3 IMAGE ENHANCEMENT

The procedures applied to image data in order to more effectively display or record the data for subsequent visual interpretation. Normally, image enhancement involves techniques for increasing the visual distinctions between features in a scene.

Contrast Stretching

Contrast Stretching is done such that the required features will be more clearly visible in the satellite images. The breakpoint of each band of the image is adjusted in the ERDAS IMAGINE so that roads are more clearly visible. For each band of multi-spectral images the breakpoints are adjusted and checked whether the roads are visible. The resultant image will gives a better idea of location of roads in the images.

Fig. 10 Contrast Stretched Image

High-pass Filtering

A simple high pass filter may be implemented by subtracting a low pass filtered image (pixel by pixel) from the original, unprocessed image. The high frequency image obtained after high pass filtering will have a high contrast and gives a better idea of roads. The image will be sharpened and it roads will be more clear.

23

Fig. 11 High-pass Filtered Image

Edge Enhancement

In ERDAS IMAGINE, the high frequency image is passed through a Kernel of size 3x3 and a high frequency image is produced containing the edge information. The composite image is then contrast stretched. This image is a high frequency sharpened image. The edges of roads will be clearer in these images. This image clearly gives the details of roads in the study area for their extraction. This road details are then digitized in Arc GIS.

Fig. 12 Edge Enhanced Image

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4.4 DIGITISING OF ENHANCED IMAGES

The processed image is then loaded in ArcGIS for the extraction of roads. The roads are digitized by visual interpretation and saved as corresponding feature class for each image. A road passing through an area with uniformly distributed vegetation, like paddy field becomes prominent due to their different reflection characteristics. The areas where there is a very good background contrast then the road section throughout and edges of the road can be identified clearly. From the selected 16 wards of Trivandrum corporation 75km length of road is digitised.

Fig. 13 Digitised Road Map

25

CHAPTER 5

MAP REVISION BY FIELD DATA

The missing road networks from the satellite image due to various reasons like

the resolution of image, canopy cover, single band image, narrow width of roads etc,

are to be incorporated to the digitised road map by using collected GPS and EDM

data of the corresponding roads in ArcGIS.

5.1 GPS DATA

Roads having smaller width were not able to digitize in ArcGIS. Those roads

can be plotted using hand held GPS. The readings, latitudes and longitudes, of roads

were taken manually by field investigation and need to be added to the missing links

manually.

Table 2 GPS Coordinates of Missing Roads

LOCATION LAT LONG

Pattom 76°56´34´´ 8°31´1´´

76°56´38´´ 8°31´1´´

76°56´38´´ 8°31´5´´

76°56´38´´ 8°31´8´´

76°56´42´´ 8°31´8´´

76°56´42´´ 8°31´5´´

76°56´46´´ 8°31´5´´

76°56´46´´ 8°31´1´´

76°56´42´´ 8°31´1´´

76°56´42´´ 8°30´58´´

76°56´42´´ 8°30´54´´

26

5.2 EDM DATA

The widths of extracted roads are to be measured using Electronic Distance Meter (EDM) at various locations and the average value is assigned as the uniform value.

Table 3 Road Widths at Junctions

Junction Road Width (m)

Plamood Manchadivila 6.5

Plamood to PMG (One Way) 7

PMG to Plamood (One Way) 8

Varambasseri 5.5

Pattom 14

PMG Barton Hill 9

Museum 15

Palayam 17

Museum Vellayambalam 15

Nanthancode 7

Palayam 13.5

PMG 15

Vellayambalam Museum 15

Thiruvananthaapuram – Thenmala

15

Shasthamangalam 18

Peroorkada 14.55

27

Palayam Statue 18.5

PMG 18.5

Bakery Fly Over 21

Kerala University 23

Peroorkada Ambalamukku 13.5

Main Central 15

Kesavadasapuram Ulloor 10

Main Central 15

Pattom 14

Ayurveda College Statue 15

East Fort 15

LMS PMG 18

Palayam 14

Vellayambalam 15

Kawdiar Peroorkada 13.5

Pattom 13

Vellayambalam 13.5

Pattom Kesavadasapuram 14

Kawdiar 13

PMG 14

Medical College 7

28

Kerala University General Hospital 13

Bakery Junction 21

VJT Hall 10

PMG 6

General Hoapital Vanchiyoor 10

MG 8

Pattoor 14

Patoor Palayam-Airport 14

Kanammoola Palam 5

Pallimukku Palayam-Airport 14

Kanammoola Palam 5

Kanammoola SBI

Medical College 7

PMG 6.5

Statue Ambujavilasam 6

Press 7

29

Fig. 14 Digitized Road Network of Selected Area with GPS Data Incorporated

30

CHAPTER 6

CONCLUSION

The road map preparation using conventional methods is a tedious and time

consuming task. As the transportation facilities in the developed as well as developing

countries change at very faster rate new methods of road map preparation that make

use of the information technology is need of the time. Road extraction from satellite

image can play an important role in the map revision processes. The software like

ERDAS Imagine and Arc GIS, and Geospatial data collection instruments like GPS,

and EDM helps in the extraction of road network of an area from a satellite image

which can be used to update maps at a faster rate.

The main advantage of the approach used for the preparation of road network

using satellite imagery and other geospatial data collection mechanisms is easiness of

the work and the reduced time. Software like ERDAS Imagine saves a lot of time in

the map making process as it provides a great help in the rectification and restoration

of satellite images and further enhancement process of the image for the delineation

of the linear features like road network of an area. A geographic information system

has the power to incorporate different thematic layers of geo-spatial data and integrate

it with the non spatial data. A GIS based road network, as prepared in this work, will

facilitate further manipulation and easy updating. It can also be used for the decision

makers by employing a suitable analysis with the data.

The accuracy of the work is mainly determined by the resolution of the

satellite image used. The available high resolution image in the Department of Civil

Engineering was Cartosat image with a spatial resolution of 2.5m which was a single

band image. The results show that the width of the roads that can be extracted from

the satellite image has a relation to the spatial resolution of the data. In the present

work roads having width smaller than 5m, which is two times the spatial resolution of

the image, could not be identified in the extraction process.

Road map preparation using satellite images can eliminate a lot errors

associated with the conventional map making using field survey, especially the

inherent errors associated with the conventional plotting can be eliminated by

automatic extraction and further digitising in GIS.

31

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