automatic digitization. In the context of ever increasing population worldwide and thereby

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1 Chapter 1 Introduction In the recent time, many researchers had thrust upon developing various improvised methods of automatic digitization. In the context of ever increasing population worldwide and thereby reduction in resources, such as land, water, infrastructure etc., there is a need for optimal use of natural resources for sustainable management for the present and planning for the future. Geoinformatics, the recently developed field of computer science and engineering plays a vital role in planning, management and decision making for natural resource management. The tools for Geoinformatics consist of two parts viz; Remote Sensing (RS) and Geographic Information System (GIS). Remote sensing is defined as the process of acquiring information about an object or phenomenon without making physical contact with the object of interest [38]. GIS is a special purpose system designed for managing spatial database. The sub-processes that are involved in GIS are information acquisition, archiving, manipulation, query, analysis and presentation [22]. In GIS, the first and the foremost task is to generate various layers of thematic data to be combined or manipulated at a later stage by the decision makers to arrive at a need based conclusions [23]. In generating various thematic layers, the basic task is digitization. Manual digitization as practiced widely is a technique used in-order to capture, organize and store 1

2 information regarding various identifiable features that are present in a reference map. Usually manual digitization processes are time consuming and cost critical. These techniques are used as preprocessing tools while preparing data for analysis. The effectiveness of the inferential studies depends to a great extent on the outcomes of the digitization process. In this fast developing world, the decisions are needed to be faster and accurate. This necessitates the introduction of automatic digitization in GIS analysis. Automatic digitization performs the same task as manual process but with minimum and/or no human interference. These techniques can be developed for all possible features extracted from GIS data. Due to advancement in technology in the recent time, the systems are capable of sustaining complex applications ensuring increased throughput. Digitization techniques effectively utilize the processing ability of these systems to generate quality knowledge-base for analysis. The effectiveness of the automatic digitization relies on the choice of efficient algorithms and information coding done for the same. While implementing such procedures, a thorough study of the various feature are essential for acquiring sufficient knowledge regarding the same. Study of various features would help in identifying unique signature for different features which can further be used as the basis for identifying the respective features. After identifying the features and their corresponding signature sets, these digitization techniques can further be advanced to create representations for the attributes and store them efficiently in 2

3 the repository. These repositories can be used as a basis for analysis and/or can be considered as source of information for further processes. The efficiency of the digitization algorithms mainly depends on the following factors. the type of constructs designed for identifying different features the type of data structure used for storing the various identifiable features and its ability in enabling the algorithm to perform the tasks efficiently the type of data retrieval technique used for accessing the reference data or the stored data The overall complexity of the digitization process is defined and contributed by the above three components. Results generated using such automated techniques ensures accuracy, quality, reliability and cost effectiveness. The main advantage of automatic digitization [20] lies in minimization of manual work and thus the human induced error occurred during manual digitization. It also reduces the time and cost required to implement these processes. 1.1 Reference Map/Data A reference map/ data is a vital input to the digitization process. A reference map may be a topographic map (SOI map) or geo-referenced satellite imagery or an engineering drawing etc. 3

4 1.2 Digitization Irrespective of its role in thematic analysis, digitization from the view point of computer science and engineering refers to a process of representing the aspect of interest using a discrete collection of points. It is the process of translating analog data (hard copy) into digital form for computer manipulation to derive thematic inferences. In case of digital data as reference map, digitization becomes a process translation of digital data from one form to another. Digitization process creates virtual copy that highlights a feature of interest by considering related knowledge such as pixel intensity etc. as basis for selecting a particular feature. These virtual copies are further used for deriving inferences. Digitization process can be categorized as described here under [21] Manual Digitization Method Manual digitization is a process where a tablet is used for projecting the reference map, and then the features are manually digitized. Such digitization process is time consuming and also heavily relies on the human skills and would prove effective only if executed by experts Heads-Up Digitization Method Heads-up digitization tries to overcome the problem associated with manual digitization by allowing direct digitization of a feature on the screen where the reference map is projected. The accuracy and confidence of the result obtained can be improved to a certain level of acceptance but even this technique suffers from fundamental problem of increased time consumption. 4

5 1.2.3 Interactive Tracing Method Interactive tracing method is an improvement over the heads-up digitization to ensure digitation ease and better accuracy. Even in this case, uncertainties due to human interaction persist as in other cases Automatic Digitization Method Automatic digitization method tries to overcome the problems associated with all the above stated techniques by reducing and/or eliminating the extent of human participation during digitization [20]. Automatic digitization method relies on development of procedures which if provided with certain attribute-related information, can efficiently digitize the attributes. The reduction of human interaction leads to a semi-automatic digitization, while its elimination leads to completely automatic digitization. Due to reduced human participation in such digitization process, the confidence of the results obtained is qualitatively high at the cost of lesser time and effort. Automatic digitization processes usually rely on the image processing techniques and pattern recognition concepts. 1.3 Purpose for implementation Morphological and other thematic features that can be digitized using either of the digitizing techniques can be broadly classified into these four categories points, lines, polygons and annotations [19]. Feature representation of data pertaining to any area of interest may consist of different types of superimposed layers of information. Each layer is capable of conveying valuable information regarding one unique feature aspect. These aspects generally include contours, drainage system, transportation network, landmarks and text etc. Research works pertaining to morphological features may require few or all of these aspects as knowledge base 5

6 for conceiving decisive inferences. In order to reduce the complexity involved in data interpretation, the various aspects of interest are generally represented using different distinctive color code. These color codes are usually prominent, intuitive and can easily be differentiated by normal human eye. However, for an automated system to differentiate these feature, sufficient knowledge or signature detail regarding the various attributes need to be provided to the system in advance to enable it to extract the details pertaining to the same. A suitable data segmentation scheme is very essential to differentiate various superimposed layers efficiently. Inferences or conclusions of various research works related to thematic/morphological studies completely relies on the result of digitization process which necessitates that the result of digitization process should be highly accurate to conclude with meaningful inferences. Traditional manual techniques used for digitization of thematic/morphological features suffer severely from following inherent problems. demands high skill and sufficient past experiences for differentiating various features with relative ease. In absence of such resources, the result generated would be highly incorrect needs greater amount of manual effort. The tedious process of manually selecting various point pertaining to a feature would incur tremendous amount of time and are usually not preferred when there is a schedule crunch even after investing increased time and effort, the results generated may be highly imprecise, even for the minute errors committed at any stage of digitization which may have greater impact on inferential studies needs greater infrastructural setup suffers from human induced error 6

7 the processes being usually restricted to feature identification and creation of feature layers, the creation of repository for storing the attributes of the identified feature would prove difficult On the contrary, the automatic digitization process suffers from neither of the above highlighted problems. Automatic digitization process may demand greater effort and time during the initial conceptualization and design phase. However, once implemented successfully it can be molded into a generic process that can be further reused. In addition to feature identification the process can also be extended for creating repository for storing the attribute values of the various identified feature in a precise manner. 1.4 Type of features A reference data can host different types of features. These features are broadly classified into four major categories as discussed below. Point A point is a feature in a map that has neither length nor an area at a particular scale [18], and hence starts and ends at same coordinate point with no intermediate coordinates. It is generally used to represent a geographic location of interest, such as landmarks and reference points etc. [17]. Line A line is a two dimensional feature in the map that has length but does not have area at a particular scale [18]. Line feature can be of different types, such as short line or a ridge, complete line or a ridge, line or ridge ending etc. Features associated with a line may be a 7

8 representation of road network, drainage system, bifurcations, spur, crossover or bridge, delta and a core. Line feature can be of different types: Short line or a ridge: it is an independent line or ridge that originates and ends at non boundary coordinates. Complete line or a ridge: it originates at one of the boundary coordinates runs across the image and then terminates at another boundary coordinate. Ridge ending: it originates at one of the boundary coordinates and abruptly ends after running for some time. The generic features of a line can be stated as follows. Bifurcation is a split or division of a line into two or more lines. These are generally encountered in drainage pattern and transportation network, which play an influential role in analyzing various networks. Spur refers to a small line or a ridge branching from a long line or a ridge. Spurs are generally encountered in river pattern where a tributary joins the main stream. Crossover or bridge refers to line or a ridge that runs across a set of parallel lines. These are generally encountered at places where drainage system overlaps contours. Delta is a joining of two or more ridges to form another ridge. Morphologically delta and bifurcation look alike. Delta is a join where bifurcation is a split. Core refers to a U-turn in the ridge pattern. These are generally encountered along contour pattern. 8

9 Polygon A feature that bounds an area at a particular scale [18] i.e., a feature that originates and ends at same coordinate with intermediate coordinates. A polygon may be used to represent various features such as contours, land-use and land-cover etc. Text: Texts are included in reference map as attributes to the features such as land marks and elevation detail with the contours [16]. 1.5 Objective The main objective of this Ph. D. dissertation is to design and develop efficient automatic digitization techniques to obtain digitized thematic/morphological layers with high degree of accuracy and confidence at faster speed. Emphases are given for software algorithmic development, keeping aside the hardware implementations. This work implements automated procedures for data traversal, reconnection, digitization, identification, tracking attributes and creating repository related to various features with special emphasis to morphological studies. 1.6 Motivation From the extensive literature survey and review, as later discussed in Chapter-2, it can be easily inferred that the manual digitization technique suffers from several inherent demerits, such as increased time, effort and cost requirement [20]. Due to increased human participation in digitization process, dependability and reliability of the results obtained are highly questionable. In order to overcome these limitations, it appears most essential that automatic digitization techniques can be developed to reduce time and cost of digitization and the uncertainties due to 9

10 human factor to achieve high quality and reliability [20]. Complexity management is one key aspect of such techniques. Implementation of such automatic procedures highly demands quality information coding techniques as well as the selection of a suitable mechanism for storing the information pertaining to the digitized data. 1.7 Proposed Solution Strategies This research work in abstraction has been broadly factored into three identifiable modules viz; first module for image interpretation second module for feature identification third for representing digitized data set The schema in the Figure 1.1 outlines the solution strategy adapted for the study. 10

11 Figure 1.1: Solution Strategy 11

12 1.8 Thesis Contribution The contribution of this Ph. D. work mainly emphasizes on design and development of automatic digitization techniques for the following. extracting and identifying various features with reduced time, effort and ease automatic classification and association of potential attributes with the identified features designing improvised procedures for identification of potential features creation of data repository for identified features 1.9 Potential for applications Automated digitization techniques may be used in various disciplines of technological sciences as described below. Geographic Information System (GIS) GIS based analysis of various morphological/thematic features such as contours, road/rail/river networks and landmark along with textual descriptions. Biometrics Extraction of features related to various physiological biometric such as finger print, retina and DNA which can be compared with captured image for identification and correlation. Information retrieval systems Extraction of valuable information from doodles pertaining to aged artifacts as well as artifacts prepared using standard fonts. and many other applications. 12

13 1.10 Organization of the Thesis The contents of the thesis have been summarized in seven chapters, as discussed below. Chapter 1: Introduction Chapter 1 as detailed above, presents a preview of the motive of the research initiative. It highlights and discusses various types of digitization schema, the importance and significances of automatic digitization, possible identifiable features, digitization complexities and the proposed solution strategies. It also discusses the motivation and possible application domain with the layout of the thesis. Chapter 2: Literature Survey Chapter 2 discusses the various citations that have been referred for acquiring knowledge regarding the contributions to the field of automatic digitization. This chapter describes the conclusions framed by various researchers regarding different features of interest, their limitations and future scope of studies to build the knowledge base for executing the research work. Chapter 3: Automatic Digitization of River Morphology Chapter 3 discusses algorithm proposed and designed for extraction of river pattern using spiral traversal and implementation of an automated algorithm for classifying stream using the knowledge of Horton s classification scheme and Strahler s classification scheme. It also discusses ability of the algorithm to create repository that highlights different attributes such as source and destination, bifurcation ratio, length and order of each identifiable streams of the drainage pattern and creates a repository for the same. This algorithm may be used for any type of thematic networks. 13

14 Chapter 4: Automatic Line Reconnection In digitization, broken lines most often lead to discontinuity of features such as line and polygon etc. It becomes essential to connect these broken lines for the digitization to be efficient. In Chapter 4, four different algorithms have been proposed and developed in order to establish continuity of the broken lines that might be possibly digitized into any of the potential feature. The proposed algorithms for maintaining continuity are developed using the concept of Leech approach, Directional Flow approach, Wiper approach and Water flow approach. The effectiveness of each of these algorithms is also evaluated. Chapter 5: Automatic Extraction of Minutiae and Automatic Contour Regeneration Chapter 5 discusses the algorithm used for identifying points and line features. Lines and points are crucial feature in any morphological reference data and form parts of several others features. In detail this chapter discusses different procedures highlighting various concepts used for extracting and classifying different types of lines and points. It also identify various attributes such as length, start and end coordinate and type of line, with the help of efficient spiral traversal scheme and creates a repository for the same. In addition it discusses algorithm designed for identifying various enclosures and islands present in the topographic map. Chapter 6: Text Recognition and Correction Chapter 6 discusses the algorithms designed for extracting various forms of textual details present in the topographic map. A reference map basically consists of two types of text features namely numbers for representing elevation and position etc. and words names of the 14

15 features. In this chapter, two algorithms are implemented by using the concept of friend repetition pattern of the pixels with minimum and maximum repetitions respectively. Chapter 7: Summary and conclusion Chapter 7 summarizes the research work. It discusses the procedures implemented for digitizing various features with an emphasis on algorithmic efficiency and effectiveness of data representation including the limitations and application scopes. It also highlights the possible extensions for future research. 15