Abstract
How to obtain accurate difference map remains an open challenge in change detection. To tackle this problem, we propose a change detection method based on saliency detection and wavelet transformation. We do frequency-tuned saliency detection in initial difference image (IDI) obtained by logarithm ratio to get a salient difference image (SDI). Then, we calculate local entropy of SDI to obtain an entropic salient difference image (ESDI). The final difference image (FDI) is the wavelet fusion of IDI and ESDI, and Otsu thresholding is used to extract difference map from FDI. Experimental results validate the effectiveness and feasibility.
Highlights
Land cover change detection is to identify differences in the state of an object or phenomenon by observing it at different times (Singh, 1989)
We address the problem of detecting accurate difference map using saliency model
We apply a bilateral filter to DL to reduce noise, and obtain the initial difference image (IDI) DI
Summary
Land cover change detection is to identify differences in the state of an object or phenomenon by observing it at different times (Singh, 1989) It is a fundamental problem in multi-temporal remote sensing image analysis and plays an important role in both civil and military applications, such as agricultural survey (Bruzzone and Serpico, 1997), forest monitoring (Hame et al, 1998), natural disaster monitoring (Di Martino et al, 2007), urban change analysis (Ridd and Liu, 1998), military reconnaissance etc. Some researchers (Li, 2009, Yu, 2013) introduced Itti visual attention model (Itti et al, 1998) to change detection They computed difference map directly on the saliency map, and weakened the details of changed region.
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