Fully Size-Adaptive Sliding Window Method for SAR Image Edge Detection
Jiamu Li, Yi Wang, Zijian Wang, Wenbo Yu, Zhongjun Yu · IEEE Geoscience and Remote Sensing Letters · 2022
The local statistics are useful for the synthetic aperture radar (SAR) image processing. However, unreliable statistical results are produced due to insufficient sample size. To overcome the statistical limitation, the appropriate size of sliding window is vital in the image processing. Therefore, a completely size-adaptive sliding window method is proposed in this letter. A method to determine the size boundaries of the sliding window based on the basic size information is firstly proposed. Then, a strategy for changing the sliding window size is used to generate the sliding window size index matrix of the image. Among the many image processing applications that use sliding windows, we choose the SAR image edge detection arbitrarily to verify the effectiveness of the proposed method. And the experimental results indicate that our method makes the original edge detection algorithms improved significantly. Further, the proposed size-adaptive sliding window method has potential for many other image processing tasks that require the use of local statistical information.