PREPARATION OF ROAD NETWORK FROM SATELLITE IMAGERY

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PREPARATION OF ROAD NETWORK FROM SATELLITE IMAGERY

  1. 1. 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 For this reason, maps are typically out of date.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. 2. 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. 3. 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. 4. 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. 5. 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. 6. 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. 7. 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. 8. 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. 9. 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
  10. 10. 10 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.
  11. 11. 11 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. 12. 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. 13. 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
  14. 14. 14 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. 15. 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
  16. 16. 16 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.
  17. 17. 17 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.
  18. 18. 18 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. 19. 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. 20. 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. 21. 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
  22. 22. 22 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. 23. 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
  24. 24. 24 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. 25. 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. 26. 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. 27. 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. 28. 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. 29. 29 Fig. 14 Digitized Road Network of Selected Area with GPS Data Incorporated
  30. 30. 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. 31. 31 REFERENCE 1. Ana Paula Camargo (2001). “The Uses of GPS in Civil Engineering as a Tool for Monitoring Structural Oscillations of Bridges” 2. Heng Lia, Zhen Chenb, Liang Yonga, Stephen C.W. Kongc (2004). “Application of integrated GPS and GIS technology for reducing construction waste and improving construction efficiency” 3. Jun Zhou, Walter F. Bischof Terry and Caelli (2006). “Road tracking in aerial images based on human–computer interaction and Bayesian filtering” 4. Karthika (2011). “Effect of spatial and spectral resolution on the extraction of road network” 5. Lillesand T. M., Kiefer R. W., John Wiley and sons (1979). “Remote sensing and image interpretation” 6. Mena J.B., Malpica J.A. (2005). “An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery” 7. Paul M. Mather (2000). “Computer Processing of Remotely-Sensed Images An Introduction”

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