期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 54, 期 1, 页码 609-625出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2015.2463075
关键词
Building detection; change detection; high resolution; morphological
类别
资金
- National Natural Science Foundation of China [91338111]
- China National Science Fund for Excellent Young Scholars
- Foundation for the Author of National Excellent Doctoral Dissertation of PR China [201348]
The new generation of Earth observation sensors with high spatial resolution can provide detailed information for change detection. The widely used methods for high-resolution image change detection rely on textural/structural features. However, these spatial features always produce high-dimensional data space since they are related to a series of parameters, e.g., window sizes and directions. Machine learning methods are also commonly employed, but their performances are subject to the quantity and quality of the training samples, and hence, much effort should be made to collect the high-quality samples. To address these problems, in this study, a novel multiindex automatic change detection method is proposed for the high-resolution imagery. The notable advantages of the proposed model include the following: 1) Complicated urban scenes are represented by a set of low dimensional but semantic information indexes, replacing the high-dimensional but low-level features (e.g., textural and structural features), and 2) the change detection model is carried out automatically without using training samples since the information indexes can directly indicate the primitive urban classes. The multiindex representation refers to the enhanced vegetation index, the water index, and the recently developed morphological building index. Experiments were conducted on the multitemporal WorldView-2 images over Shenzhen City (south of China) and Kuala Lumpur (the capital of Malaysia), where promising results were achieved by the proposed method. Moreover, the traditional methods based on the state-of-the-art textural/morphological features were also implemented for the purpose of comparison, which further validates the advantages of our proposed model.
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