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:: [Thursday, June 9, 1994]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: [Session K-1 WG II/4 - Systems for the Processing of Radar Data - Part B]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: Evaluation of spectral classifiers for separating Sea Ice from open water in preparation for Radarsat. T. Heacock, T. Hirose, M. Manore

Document type:: Monograph

Structure type:: Chapter

~ienne à la i. On niques ar les vastes (e pas | étre blème. nt été llites Lreurs ner la champs Ces on des 1881 à cision aphies océdés apport ésager de la Evaluation of spectral classifiers for separating Sea Ice from open water in preparation for Radarsat. T. Heacock!, T. Hirose!, M. Manore? 1. Noetix Research Inc. 9^ Floor, 280 Albert St. Ottawa, Ontario, Canada, K1P 5G8. 2. Canada Centre for Remote Sensing (CCRS), Ice Applications Group, 588 Booth St, Ottawa, Ontario, Canada, K1A 0Y7. ABSTRACT The launch of Radarsat in 1995 confirms Canada's commitment to the use of spaceborne radar data for the monitoring of its land and ocean areas. Large volumes of data will be used by the sea ice community, and in particular the Ice Centre Environment Canada to monitor the shipping lanes in and around Canada. In an effort to assist in data analysis automated systems are being developed to extract value-added information products for end users. To date, research has been conducted on methods of extracting ice information from both calibrated and uncalibrated SAR data. The first step in the development of a fully automated system is the separation of areas of ice and water. Five algorithms that use only tone and local scene texture extracted from an uncalibrated SAR scene (so called spectral algorithms) were evaluated. These algorithms, representative of all spectral algorithms, were selected because of their computational speed and efficiency. The results illustrated that all the algorithms have the ability to separate ice from water in SAR imagery under ideal conditions. The performance varied on an image to image basis dependant on the amount of systematic or geophysical variability within each scene used for the evaluation. Observations about the relative strengths and weaknesses of the algorithms are made. KEY WORDS Synthetic Aperture Radar, Sea Ice, Classification, Algorithm, Tone, Texture. Environment Canada (ICEC), and it's client services 1.0 INTRODUCTION Remotely sensed data is a practical and cost-effective method of acquiring detailed, timely information over Canada's ice-infested waters. With the launch of RADARSAT in early 1995 (Langham 1992), large volumes of Synthetic Aperture Radar (SAR) data will be acquired over Canada's oceans. It is anticipated that manual analysis of the data on a regional scale will be overwhelming. To overcome this potential bottleneck, researchers have proposed the development of automated systems to generate products for end users in all applications disciplines, and in particular for sea ice applications because of the operational nature of the Ice Centre, (Ramsay et al. 1993). An automated system for classifying Radarsat imagery must be able operate on data acquired from a single channel, single polarized SAR (C-band hh). The system must be robust enough to operate on imagery illustrating a diversity of ice conditions, collected from all seasons and all geographic regions of operational interest to ICEC. Furthermore, the data product which will be used by ICEC operations will be ScanSAR data, generated through a quick delivery system. This data will contain image ambiguities that are a function of the unique nature of the ScanSAR image creation process. To date, ice classification research has been carried out for both calibrated (Kwok et al. 1992) and 427

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