Method of urban land change detection that is based on GF-2 high-resolution RS images

ABSTRACT

With the successful launch of China’s high spatial resolution satellite Gaofen-2 (GF-2), the use of high spatial resolution satellite images for land change detection has high research potential. Based on the images from GF-2, this study combines principal component analysis and the spectral feature change method to identify different land changes in the form of different coloured patches. Then, three decision tree classification models are constructed to automatically detect the change, which includes information on the increase in the number of airports and buildings and increased or decreased vegetation. Further, through Quick Bird images for identical regions in the same periods, a sample of 2624 pixels is selected using a stratified random sampling method to verify the accuracy of the results indicating a change. The results show that the overall accuracy of the extracted information on land change was 98.21%, and the Kappa coefficient was 0.9604. Therefore, the method for land change detection and extraction of land change information used in this study is proven to be effective.

KEYWORDS:

• GF-2
• spectral characteristic variation
• principal component analysis
• decision tree
• change detection

Acknowledgments

The authors thank reviewers for their useful comments and suggestions that helped improve the quality of this paper.

Additional information

Funding

The study is supported by the funding from Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, National Key Research and Development Program of China (No.2017YFB0503901).