Pointwise SAR image change detection based on stereograph model with multiple-span neighbourhood information

ABSTRACT

In this article, a novel pointwise approach is proposed for change detection in bi-temporal synthetic aperture radar (SAR) images using stereograph model. Due to the fact that SAR image suffers from the speckle noise, a pointwise approach based on a set of characteristic points only, not on the whole pixels, seems to be more efficient. Moreover, the correlations of neighbourhood points which have different locations in bi-temporal SAR images should be studied to repress the speckle in change detection. Therefore, the stereograph model, which extends the graph model to three-dimensional space, is designed to connect the local maximum pixels on bi-temporal SAR images and can be used to capture the multiple-span neighbourhood information from the edges. Furthermore, a specialized change measure function is presented to quantify the neighbourhood information from stereograph model, and thus, a novel nondense difference image (NDI) is generated. Finally, a traditional classification method is used to analyse the NDI into changed class and unchanged class. Experiments on real SAR images show that the proposed NDI can improve separability between changed and unchanged areas, and the final results possess high accuracy and strong noise immunity for change detection tasks with noise-contaminated SAR images.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61371154, 41601452, and 61701154), Key Research and Development Program of Anhui Province (1704a0802124), the Programs of Anhui Key Research and Development (1608085QF142), and the Fundamental Research Funds for the Central Universities (JZ2017YYPY0237).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41601452, 61371154, 61701154]; Fundamental Research Funds for the Central Universities [JZ2017YYPY0237]; Programs of Anhui Key Research and Development [1608085QF142]; and Key Research and Development Program of Anhui Province [1704a0802124].