Mr. Chen Pan Profile

Chen Pan

at Tongji Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 30 October 2009 Paper

Extraction and classification of urban land cover based on multisource data and their applications in Chongming

Chen Pan, Yi Lin, Yingying Chen

Proceedings Volume 7498, 74982Z (2009) https://doi.org/10.1117/12.832592

KEYWORDS: Remote sensing, Image classification, Image fusion, Digital imaging, Feature extraction, Library classification systems, Volume rendering, Mathematical modeling, Image resolution, Roads

Read Abstract +

The distribution and allocation of different land cover classes is related with resource exploitations, environmental pollution controlment and human habitat environment quality. In this paper, the multi-source data including high resolution RS imagery-FORMOSAT-2 image and digital topographic maps are applied to acquire the information of urban land cover by taking Chongming as a case study. Firstly, the overall framework is proposed to apply multi-source data to extract and classify urban land covers. Then, some classes of land cover are extracted and the high resolution RS imagery is classified based on C5.0 decision tree classifier. In the feature library of different urban land covers established, there are three features: spectral feature, texture feature, and shape information. Spectral and texture features are acquired from the RS imagery, and shape information is computed from digital vector maps using ArcGIS. Based on multi-feature, the classification model via C5.0 decision tree is constructed to realize the urban land cover classification and extract different land cover classes. Finally, classification accuracy and results are compared between this method and other conventional classification methods. This method proves to improve the classification accuracy more effectively.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years