Earticle

초록

영어

Based on the gray features and shape features of objects, some satisfied objects are detected by using sliding window algorithm from satellite image. To further recognize their identification and classification, more texture features of them are needed to obtain to compare between them. GLCM (Gray-Level Co-occurrence Matrix) statistics are used to representative each partition of them. These PGLCM (Partition-GLCM) statistics can combine into a feature vector and those detected objects can be accurately recognized and classified by using GLVQ (Generalized Learning Vector Quantization) Neural Network algorithm. Experiments show when we choose those adapted parameters, such as the length and width of the window, and the threshold of difference of adjacent pixels, the extraction rate of building objects is up to 76.1%. Using the classification algorithm based on the feature vector generating by the statistics of PGLCM, the recognition rate of building is more than 88.9%.

목차

Abstract
 1. Introduction
 2. Research Statuses
 3. Building Extracting Algorithm based on Satellite Images
 4. GLCM Statistics Extracting and Classification Algorithm
 5. Experiment Design and Analysis
 6. Conclusions
 References

키워드

저자

• Fan Yang [ School of Information Engineering, HuangShan University, Anhui, China ]
• Laixin Shen [ School of Information Engineering, HuangShan University, Anhui, China ] 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