Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WP-4 INTERPRETATION AND MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: AUTOMATED SYSTEM FOR IMAGE INTEGRATION IN REMOTE SENSING. Malcolm E. Fuller & Manfred Ehlers

Document type:: Multivolume work

Structure type:: Chapter

445 AUTOMATED SYSTEM FOR IMAGE INTEGRATION IN REMOTE SENSING Malcolm E. Fuller & Manfred Ehlers Department of Surveying Engineering University of Maine Orono, Maine 04469-0110 E-mail: ehlers@mecanl.bitnet ABSTRACT In the integration of remotely sensed multisensor, multitemporal, multispectral, and multiresolution image data, one of the more time-consuming tasks has been the rectification of different images to a mutual geometric basis. This is due to the difficulty of automating the process of selecting adequately distributed and identifiable control points. This paper outlines such an automated process involving: control point identification, matching, and correlation. Result of preliminary tests applied to the system are also reported. KEY WORDS: image matching, Moravec operator, partitioning, correspondence, correlation, fine tuning. INTRODUCTION An important step in the integration of imagery is placing images in exact pixel by pixel registration with each other. There are many integrative applications (e.g. modeling global environmental cycles, multitemporal change detection, GIS integration) which require this process. However, to date it has remained one of the few image processing functions which have not been automated. Image registration still requires, to a large degree, time-consuming human input for the selection and matching of control points (CPs) in the images to co-register. An automated image registration approach would make the study of multisensor, multitemporal, multiresolution, and multispectral image datasets much more efficient. This is particularly true as the size of these datasets increase to include more information, and computers become powerful enough to handle such a large quantity of data. Our research in the area of image registration has been directed toward developing a prototype software package which will select, match, and fine tune CPs with as little human interactive input as possible (i.e. the user should only have to enter input/output file names along with some processing parameters). This paper will explain the approach taken for this process, as well as a description of the prototype software package as it currently stands. Preliminary test results are also included. METHODOLOGY OF AUTOMATED IMAGE REGISTRATION In this paper, an image which is already in a desired geometric basis (e.g. rectified to UTM coordinates) is called the reference image. An image which is not in a desired geometry (e.g. raw scanned data), and must be transformed in order to match the reference image is called the subject image.The initial strategy taken toward the registration of a subject image to a reference image is similar to one described by Luhmann and Ehlers [1984] in the development of an image matching system. The process of co-registering images is broken up into six steps: 1 .Preprocessing of the images, which may include filtering, scale change, window definition and other corrections to enhance the matching process. 2.Selection of candidate control points (CCPs) in each image to co-register. 3. Matching of the CCPs in each image, to determine which points in the reference image correspond to those in the subject image. 4. Fine tuning of the matched CCPs to determine the actual location of points in the subject image to subpixel accuracy. 5. Calculation of the rectification coefficients for the transformation from reference to subject image using the fine-tuned control points (CPs). 6. Resampling of the subject image into the geometry of the reference image. In all of these steps except the first and the last, CCPs which have been determined to be ambiguous or otherwise unsuitable as control points are eliminated from further consideration. At the end of the process there should be considerably fewer CCPs than were chosen initially, but those points should be adequate for high-precision image registration. CCP selection A human interpreter normally tries to choose control points in locations which contain easily identifiable surrounding texture and contrast (e.g. road intersections, bridges, building corners). Most attempts to automate the selection process rely on functions, called interest operators, which also locate areas in a digital image with a high surrounding contrast. Unlike a human interpreter, interest operators do not attempt to find CCPs which are topographically significant [Piechel 1986]. Suitable CCPs extracted by interest operators may be located in areas where a human would not normally select control points, yet show enough local contrast in the algorithm's eye for possible use. There are several interest operators that have been developed for use in extracting suitable points. Most of these can be found in digital photogrammetry and computer vision literature [Moravec 1980, Nagel 1981, Forstner 1985, Piechel 1986, Luhmann and Altrogge 1986]. Although originally developed for robotic vision applications, the Moravec operator was implemented first in our software package because it has been shown in previous research to

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