Earticle

초록

영어

Good segmentation of satellite images plays a significant role in monitoring of urban areas, as well as of natural terrain. The analysis of two segmented observations can provide new information relating to land use, shifting cultivation, deforestation, or environmental changes. This paper introduces a combination of textural features that can achieve very good results for terrain segmentation. We implement BPNN (Back Propagation neural network) and Adaboost algorithms for the classification of an urban area in terms of a combination of several textural features. Using high resolution IKONOS satellite images of the Boston area, we carry out experiments on terrain classification. Experimental results show that a combination of co-occurrence and Harr-like features can be used to obtain high accuracy of terrain classification of 89.8-94.5% with the Adaboost classifier; this new method outperforms other implementations. To verify the efficiency of the proposed classification method, change detection using temporal images is also tested via experiment. The resulting change map shows that a newly developed area can be successfully detected.

목차

Abstract
 1. Introduction
 2. Implementation of Features
  2.1. Haar-like Implementation
  2.2. Co-Occurrence Implementation
  2.3. Combination of Features
 3. Change Detection Scheme
 4. Experiment
  4.1. Data Preparation
  4.2. Classification
  4.3. Change Detection
 5. Conclusion
 References

키워드

저자

• Hoang Lam Le [ Department of Electronics Engineering, Myongji University San 38-2 Namdong, Cheoin-gu, Yongin, Gyeonggido, Korea 449-728 ]
• Dong-Min Woo [ Department of Electronics Engineering, Myongji University San 38-2 Namdong, Cheoin-gu, Yongin, Gyeonggido, Korea 449-728 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605