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Techniques for image processing and classification in remote sensing

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Published by Academic Press in New York .
Written in English

Subjects:

  • Image processing,
  • Remote sensing

Book details:

Edition Notes

Includes bibliographies and index.

StatementRobert A. Schowengerdt.
Classifications
LC ClassificationsTA1632 .S3 1983
The Physical Object
Paginationxv, 249 p., [3] p. of plates :
Number of Pages249
ID Numbers
Open LibraryOL3169644M
ISBN 100126289808
LC Control Number83011769

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The book begins with a discussion of digital scanners and imagery, and two key mathematical concepts for image processing and classification-spatial filtering and statistical pattern recognition. This is followed by separate chapters on image processing and classification techniques that are widely used in the remote sensing : Robert A. Schowengerdt. Remote Sensing, in its third edition, seamlessly connects the art and science of earth remote sensing with the latest interpretative tools and techniques of computer-aided image processing. Newly expanded and updated, this edition delivers more of the applied scientific theory and practical results that helped the previous editions earn wide. Concept of Image Classification Computer classification of remotely sensed images involves the process of the computer program learning the relationship between the data and the information classes Important aspects of accurate classification Learning techniques Feature sets 5 GNR Dr. A. BhattacharyaFile Size: KB. Additional Physical Format: Online version: Schowengerdt, Robert A. Techniques for image processing and classification in remote sensing. New York: Academic Press,

Following the successful publication of the 1st edition in , the 2nd edition maintains its aim to provide an application-driven package of essential techniques in image processing and GIS, together with case studies for demonstration and guidance in remote sensing applications. The book therefore has a 3 in 1 structure which pinpoints the intersection between these three individual.   Image Processing and GIS for Remote Sensing the 2nd edition maintains its aim to provide an application-driven package of essential techniques in image processing and GIS, together with case studies for demonstration and guidance in remote sensing applications. The book therefore has a “3 in 1” structure which pinpoints the intersection. Introduces students to image processing & classification techniques from a remote sensing perspective. Covers fundamental mathematical concepts of image processing & classification. Surveys the image processing & classification techniques widely used in the remote sensing community. Techniques for image processing and classification in remote sensing. New York: Academic Press, (DLC) (OCoLC) Material Type: Document, Internet resource: Document Type: Internet Resource, Computer File: All Authors / Contributors: Robert A Schowengerdt.

Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition introduces techniques used in the processing of remote sensing digital imagery. It emphasizes the development and implementation of statistically motivated, data-driven by: 5. Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification by: Summary. Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed. Many advances can be seen concerning image processing techniques of enhancement, analysis and understanding from the intuitive and machine-learning level. Nevertheless, many challenges still remain in the remote sensing field which encourage new efforts and developments to better understand remote sensing images via image processing techniques.