Joint Pattern Recognition/Data Compression Concept For Erts Multispectral Imaging

Edward E. Hilbert

doi:10.1117/12.965355

30 October 1975 Joint Pattern Recognition/Data Compression Concept For Erts Multispectral Imaging

Edward E. Hilbert

Proceedings Volume 0066, Efficient Transmission of Pictorial Information; (1975) https://doi.org/10.1117/12.965355
Event: 19th Annual Technical Symposium, 1975, San Diego, United States

Abstract

This paper describes a new technique which jointly applies clustering and source encoding concepts to obtain data compression. The cluster compression technique basically uses clustering to extract features from the measurement data set which are used to describe characteristics of the entire data set. In addition, the features may be used to approximate each individual measurement vector by forming a sequence of scalar numbers which define each measurement vector in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. A description of a practical cluster compression algorithm is given and experimental results are presented to show trade-offs and characteristics of various implementations. Examples are provided which demonstrate the application of cluster compression to multispectral image data of the Earth Resources Technology Satellite.

Citation Download Citation

Edward E. Hilbert "Joint Pattern Recognition/Data Compression Concept For Erts Multispectral Imaging", Proc. SPIE 0066, Efficient Transmission of Pictorial Information, (30 October 1975); https://doi.org/10.1117/12.965355

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available