Object-Oriented Island Land Cover Change Detection by Iteratively Reweighted Multivariate Statistical Analysis

ABSTRACT

In studies of changes in island geographic information, a key topic is island land cover change detection using multitemporal remote sensing images; such information can provide good reference for island exploitation, marine resource conservation, marine economic development, and national defense building. This study proposes a fast and easy-to-implement algorithm of object-oriented change detection based on iteratively reweighted multivariate statistical analysis with high-resolution remote sensing images and vector data, improving effects of normal multivariate alternative detection by combining object-oriented technology with probability density function of chi-square distribution. This method builds vectors in feature space with spectral, texture, and spatial structure information of objects segmented based on boundaries and categories of features in vector data. By feature optimization and iterative extension to multivariate statistical analysis for change detection, change regions can be extracted. Experiments on Zhaoshu Island area show that the proposed method is highly effective and accurate in detection of change regions.

KEYWORDS:

• Change detection
• high resolution
• island and reefs
• iteratively reweighted multivariate statistical analysis
• multiscale segmentation

Funding

This work was jointly supported by China's National Science and Technology Support Program funded by the Ministry of Science and Technology (Grant No. 2012BAB16B01) and project supported by the National Natural Science Foundation of China (Grant No. 41371406, Grant No. 41330750).