Systems for data processing, anaylsis and representation

Allam, Mosaad; Plunkett, Gordon

Bibliographic data

Monograph

Persistent identifier:: 1067490280

Title:: Systems for data processing, anaylsis and representation

Sub title:: ISPRS Commission II Symposium : June 6 - 10, Ottawa, Canada

Scope:: 1 Online-Ressource (XX, 530 Seiten)

Year of publication:: 1994

Place of publication:: Ottawa

Publisher of the original:: The Surveys, Mapping and Remote Sensing, Natural Resources Canada

Identifier (digital):: 1067490280

Illustration:: Illustrationen

Signature of the source:: ZS 312(30,2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

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

Editor:: Allam, Mosaad; Plunkett, Gordon

Corporations:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Adapter:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Founder of work:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Other corporate:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Tuesday, June 7, 1994]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: [Session E-1 Intercommission WG II/III- Digital Photogrammetric Systems - Part A]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: SEMI-AUTOMATIC MONOPLOTTING ON A DIGITAL PHOTOGRAMMETRIC STATION Peggy Agouris, Dirk Stallmann, Haihong Li

Document type:: Monograph

Structure type:: Chapter

ng copic mode. ents, as well thm and ob- using wave- present the es template ly identified rced to pro- ject outline ng provides introduction 1981]: lity in prop- out particu- termination, 1 at reliable cision in de- ite approxi- able. arators from mplex com- [Suetens et 'al methods xt extraction netric semi- TING lentification task of a type | operator is performed manually on a single image, while a special automated digital mod- ule performs the tracking task of a type Il operator. More specifically, a human operator is used to iden- tify an object from an on-screen display of a digital im- age, select the particular class to which the current object belongs (e.g. road, house etc.) and provide a rough approximation of the object outline. Typically, this approximation consists of loosely identifying on- screen nodes (e.g. corners for houses, breakpoints for curvilinear objects etc.) of the outline. Subse- quently, these pieces of information are used as the necessary approximations for the automatic, precise edge positioning task. By repeating this process, any objects within an image can be identified and pre- cisely positioned. The degree of automation varies according to the extent of the required human opera- tor contribution (e.g. how many nodes have to be pro- vided for successful object extraction and how close to the actual outline breakpoints). Judging from experience in both analytical photo- grammetric data collection and digital image feature extraction, such use of a human operator within the broader object extraction strategy is considered opti- mal. Humans perform the identification task flawles- sly and almost effortlessly, and thus, their intervention optimizes achieved accuracies without imposing time burden. At the same time, the task of precise object outline positioning and tracking, which experience Shows to be the most time-consuming and error- prone part of photogrammetric data collection, is per- formed automatically in a fast and objective manner. In the next sections we will present the mathematical foundation and implementational issues for the fol- lowing semi-automatic object exraction methods: road extraction from wavelet transformed SPOT imagery, OU the use of active contour models (snakes) for outline extraction, least squares template matching, and Q globally enforced least squares template match- ing. 3. ROAD EXTRACTION USING WAVELET TRANSFORMED SPOT IMAGES The semi-automatic strategy for the extraction of road networks from SPOT images combines a wave- let decomposition for road sharpening with a linear feature extraction algorithm based on dynamic pro- gramming (Fig. 1). Wallis filter preprocessing is used to enhance the available image and facilitate subsequent object Image Preprocessing by Wallis Filter i Road Sharpening by Wavelet Transformation i Manual Selection of Seed Points a Road Detection & Tracking by Dynamic Programming Fig. 1: Semi-automatic road extraction strategy extraction processes by locally forcing the gray value mean and contrast (dynamic range) to fit certain tar- get values. Filtering is performed in image blocks of kx I pixels by modifying the original gray value g(i.j) of a pixel to a new gray value f(j,/), as [g(,j) - mg cs, (i,j) = + bm + (1-b) m, (1) where m.,, m, are the old (original) and new (target) block mean gray values respectively and s S, are the old and new block gray value standard devia- tions. The histogram enhancement parameters c and b, which respectively affect contrast and brightness, are selected such that image noise is limited. A gray value transformation for any pixel is performed by bi- linearly interpolating the filtering parameters of its four neighboring image blocks [Baltsavias, 1991]. After preprocessing, wavelet transformation is ap- plied on the image to emphasize features of interest, while suppressing other details [Grossmann & Mor- let, 1984], [Mallat, 1989]. A particular wavelet ¥() is uniquely defined in the frequency domain by specify- ing its associated filter function H(w) as © © V(o) = G(3) (>) (2) where ®(®) = [I H(27Po) (3) p=1 G(o) = 6 “H(o+n) (4) 147

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