Zoning types of mountain forest restoration in the upper Min River supported by image auto-recognition system

Q. Wang; F. C. Li

doi:10.1117/12.896501

8 July 2011 Zoning types of mountain forest restoration in the upper Min River supported by image auto-recognition system

Q. Wang, F. C. Li

Author Affiliations +

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 800914 (2011) https://doi.org/10.1117/12.896501
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

Abstract

Image auto- recognition system is a processing platform for extracting the grid data of characteristic element from original map and converting grid data to vector data. Selecting five maps about precipitation, evaporation, vegetation, soil erosion, and human activities, this paper applied images auto- recognition system and GIS technique to define and distinguish the boundary between natural restoration, human reconstruction and arid zone in the Upper Reaches of Min River. The results show that: images auto-recognition system efficiently extracted the grid data of characteristic element from original landscape patterns, and converted the grid data to vector data accurately.

Citation Download Citation

Q. Wang and F. C. Li "Zoning types of mountain forest restoration in the upper Min River supported by image auto-recognition system", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 800914 (8 July 2011); https://doi.org/10.1117/12.896501

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