Research Papers

Urban land use and land cover classification using the neural-fuzzy inference approach with Formosat-2 data

[+] Author Affiliations
Ho-Wen Chen

Department of Environmental Engineering and Management,, Chao-Yang University of Technology, Taichung, Taiwan

Ni-Bin Chang

Department of Department of Civil, Environmental, and Construction Engineering, University of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816

Ruey-Fang Yu

Department of Safety, Health and Environmental Engineering, National United University, Miao-Li, Taiwan

Yi-Wen Huang

Department of Environmental Engineering and Management, Chao-Yang University of Technology, Taichung, Taiwan

J. Appl. Remote Sens. 3(1), 033558 (October 29, 2009). doi:10.1117/1.3265995
History: Received February 8, 2009; Revised October 22, 2009; Accepted October 28, 2009; October 29, 2009; Online October 29, 2009
Text Size: A A A

Abstract

This paper presents a neural-fuzzy inference approach to identify the land use and land cover (LULC) patterns in large urban areas with the 8-meter resolution of multi-spectral images collected by Formosat-2 satellite. Texture and feature analyses support the retrieval of fuzzy rules in the context of data mining to discern the embedded LULC patterns via a neural-fuzzy inference approach. The case study for Taichung City in central Taiwan shows the application potential based on five LULC classes. With the aid of integrated fuzzy rules and a neural network model, the optimal weights associated with these achievable rules can be determined with phenomenological and theoretical implications. Through appropriate model training and validation stages with respect to a groundtruth data set, research findings clearly indicate that the proposed remote sensing technique can structure an improved screening and sequencing procedure when selecting rules for LULC classification. There is no limitation of using broad spectral bands for category separation by this method, such as the ability to reliably separate only a few (4-5) classes. This normalized difference vegetation index (NDVI)-based data mining technique has shown potential for LULC pattern recognition in different regions, and is not restricted to this sensor, location or date.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Ho-Wen Chen ; Ni-Bin Chang ; Ruey-Fang Yu and Yi-Wen Huang
"Urban land use and land cover classification using the neural-fuzzy inference approach with Formosat-2 data", J. Appl. Remote Sens. 3(1), 033558 (October 29, 2009). ; http://dx.doi.org/10.1117/1.3265995


Figures

Tables

Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.