Abstract
To improve the suppression effect for the speckle noise of synthetic aperture radar (SAR) images and the ability of spatiotemporal information preservation of the filtered image without losing the spatial resolution, a novel multitemporal filtering method based on hypothesis testing is proposed in this paper. A framework of a two-step similarity measure strategy is adopted to further enhance the filtering results. Firstly, bi-date analysis using a two-sample Kolmogorov-Smirnov (KS) test is conducted in step 1 to extract homogeneous patches for 3-D patch stacks generation. Subsequently, the similarity between patch stacks is compared by a sliding time-series likelihood ratio (STSLR) test algorithm in step 2, which utilizes the multi-dimensional data structure of the stacks to improve the accuracy of unchanged pixels detection. Finally, the filtered values are obtained by averaging the similar pixels in time-series. The experimental results and analysis of two multitemporal datasets acquired by TerraSAR-X show that the proposed method outperforms the other typical methods with regard to the overall filtering effect, especially in terms of the consistency between the filtered images and the original ones. Furthermore, the performance of the proposed method is also discussed by analyzing the results from step 1 and step 2.
Highlights
A synthetic aperture radar (SAR) is a kind of active microwave imaging radar capable of imaging all day without relying on the light source
We propose a novel method called the sliding time-series likelihood ratio (STSLR) test that is appropriate for the similarity measurement of the 3-D case, which makes full use of the multi-dimensional structure and the characteristics of time-series data to enhance the accuracy of the similar points search and reduce the misdetection rate
In comparison with traditional multitemporal speckle filtering methods, which are usually applied under the assumption that the pixels at the same coordinate position remain unchanged along the time axis, we propose a novel similarity measurement approach based on multitemporal data to extract similar pixels over time and improve the effect of the smoothness for speckle noise
Summary
A synthetic aperture radar (SAR) is a kind of active microwave imaging radar capable of imaging all day without relying on the light source. It has the advantage of penetrating clouds and rain because of the microwave band, which is able to achieve all-weather imaging. These advantages make SAR images an important data source in remote sensing applications and they are widely used in forestry monitoring [1], geological prospecting [2], disaster assessment [3], and other military or civilian fields [4,5,6]. The suppression of speckle is an important part in SAR images processing, which is of great significance for improving the image quality and promoting the wider application of SAR images
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