Dr. La Chen Profile

Dr. La Chen

at Zhejiang 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 | 5 February 2011 Paper

Assessment of a fully soft classification approach using back propagation neural network in estimating of rice growing area

La Chen

Proceedings Volume 7752, 775215 (2011) https://doi.org/10.1117/12.887191

KEYWORDS: Image classification, Remote sensing, Neural networks, Fuzzy logic, Roads, Global Positioning System, Algorithm development, Error analysis, Agriculture, Accuracy assessment

Read Abstract +

Mixed pixels are a major problem as a conventional classification will force the allocation of a mixed pixel to one class, which need not even be one of the component classes of that pixel. Since the conventional classification output is "hard", comprising of only the code of the allocated class, such techniques cannot therefore be used appropriately to represent mixed pixels. The fully soft classifications were used to accommodate mixed pixel problem at each stage of classification. More than 90% of rice is planted in southern China where population density is very high and rice planting is often conducted by unit of single firmly, thus the size of paddy field patches are very small and the shape of those are not often irregular. For estimating rice-growing field area using remotely sensing data, the mixed pixel problems are more severe. In this study, an approach to achieve such a fully soft classification using back propagation neural network (BPN) in the rice growing region was assessed. The remote sensing data used in this study is a simulated imagery from TM data and a rice field map investigated by GPS. It was found this approach can improve significantly classification accuracy for rice-growing field harden mapping and total area estimating at sub-pixel level.

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