Earticle

초록

영어

Aiming at the complexity of ground objects in urban area, and the difficulty in distinguishing ground objects using spectral characteristics, we extracted normalized different indexes, namely Modified Normalized Difference Water Index (MNDWI), Soil Adjusted Vegetation Index (SAVI ) and Normalized Difference Building Index (NDBI) , as the key auxiliary information for land use classification of urban area. To solve problems of RBF neural network, such as local minimum values and discrete output value in output layer, we used max-min distance means to initialize RBF center, and introduced equilibrium factor into Gauss function to improve RBF neural network learning algorithm. On this basis, a new urban area classification model was proposed based on improved RBF network and normalized difference indexes. At last, NanChong city in SiChuan province of China was taken as the study area, and TM images was used as experiment data to test the model proposed in this paper. The results showed that, based on the improved RBF network, with the help of spectral band information, the classification overall accuracy was 89.97%, Kappa coefficient was 0.88; using both spectral band information and normalized difference indexes, the classification overall accuracy was 95.02%, Kappa coefficient is 0.94, the classification overall accuracy was improved by 5.05%. Also, the experiment results showed that, with the help of spectral band information and normalized difference indexes, the classification overall accuracy of MLC, BP and improved RBF network was 90.12%, 93.63%, 95.02%, respectively, which means RBF has an advantage of fusing geological parameters in classification.

목차

Abstract
 1. Introduction
 2. Research Methods
  2.1. Data Preprocessing and Normalized Difference Indexes Extraction
  2.2. Improved Learning Algorithm of RBF Neural Network
  2.3. The Urban Classification Model Based on Improved RBF Network
 3. Experimental Results and Analysis
 4. Conclusions
 Acknowledgments
 References

키워드

저자

• Xiaobo Luo [ Institute of Resources and Environment, Southwest China University, Chongqing, China, Institute of Computer Science and Technology, Chongqing University of Post and Telecommunications, Chongqing China ]
• Wenya Zhao [ Chongqing Aerospace Vocational and Technology College, China ]
• Shiqiang Wei [ Institute of Resources and Environment, Southwest China University, Chongqing, China ]
• Qinghua Fu [ Pearl River Hydraulic Research Institute, Pearl River Water Resources Commission, Guangzhou, Guangdong ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605