Change detection method based on fractal model and wavelet transform for multitemporal SAR images

• Published in: International journal of applied earth observation and geoinformation Vol. 13; no. 6; pp. 863 - 872
• Main Authors: Huang, Shiqi, Cai, Xinhua, Chen, Shunxiang, Liu, Daizhi
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.12.2011; Elsevier
• Subjects: Applied geophysics; Change detection; Earth sciences; Earth, ocean, space; Exact sciences and technology; FMWT algorithm; Fractal dimension; Fractal model; GFCD algorithm; Internal geophysics; SAR image; TDDSWT; Fractal dimension; FMWT algorithm; Change detection; Fractal model; TDDSWT; GFCD algorithm; SAR image; algorithms; models; detection; sampling; fractal dimension; natural disasters; accuracy; windows; fractals; electromagnetic waves; noise; wavelet; radar methods; performances; Synthetic aperture radar; theory; direction
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2011.05.018

► Using the fractal theory describes the radar echo chaotic characteristics. ► This paper proposes a new SAR image change detection algorithm. ► The algorithm is based on the fractal model and wavelet transform (FMWT). ► One advantage is insensitive to speckle noise. ► The other advantage is that the change detection accuracy is improved. The interaction is quite complex between a ground object and an electromagnetic wave transmitted by synthetic aperture radar (SAR). In a ground resolution cell illuminated by a radar beam, there are many chaotic scatterers and the whole scattering echo has the chaotic characteristics which is usually described with the fractal theory, and the fractal dimension can be used to detect the change information for multitemporal SAR images. In order to improve the change detection effect with fractal model, this paper proposes a new multitemporal SAR image change detection algorithm based on the fractal model and wavelet transform (called FMWT algorithm). The FMWT algorithm has two advantages. One is insensitive to speckle noise; the other is that the change detection accuracy is improved, comparing with a general fractal change detection (GFCD) algorithm. Since the FMWT algorithm adopts the two-dimensional discrete stationary wavelet transform (TDDSWT) technique, it can obtain different direction sub-images and avoid down-sampling of the discrete wavelet transform (DWT). In the paper, not only the simulative data test has been carried out, but also the real natural disaster SAR images have been checked. Experimental results verify that the FMWT algorithm is feasible for multitemporal SAR image change detection, it is not sensitive to speckle noise of SAR images, and the performance of it is better than that of the GFCD algorithm. At the same time, the size of a sliding window will bring some affection in counting fractal dimensions.