Sensitivity analysis of a scattering-based nonlocal means Despeckling Algorithm

Simone, Alessio Di; Martino, Gerardo Di; Iodice, Antonio; Riccio, Daniele

doi:10.1080/22797254.2017.1274153

Cited by 5 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both the SAR-BM3D and PPB filters perform better than other methods as they allow for feature preservation and artifact reduction. Di Martino et al proposed a scattering-based non-local means method for SAR image despeckling [42,43]. Recently, bitemporal despeckling techniques have been recently introduced for SAR stacks in several kinds of applications, e.g., SAR image detection [44], SAR interferometry [45], and SAR tomography [46].…”

Section: Introductionmentioning

confidence: 99%

How Can Despeckling and Structural Features Benefit to Change Detection on Bitemporal SAR Images?

et al. 2019

View full text Add to dashboard Cite

Change detection on bitemporal synthetic aperture radar (SAR) images is a key branch of SAR image interpretation. However, it is challenging due to speckle and unavoidable registration errors within bitemporal SAR images. A key issue is whether and how despeckling and structural features can improve accuracy. In this paper, we investigate how despeckling and structural features can benefit change detection for SAR images. Several change detection methods were performed on both input images and the corresponding despeckled images, where despeckling was achieved by different methods. The comparisons demonstrate that despeckling methods that preserve the structures can suppress noise in difference images and can improve the accuracy of change detection. We also developed a sparse model to exploit structural features from the difference images while reducing the influence of misalignment between bitemporal SAR images. The comparisons were performed on five datasets of bitemporal SAR images, and the experimental results show that our proposed sparse model outperforms other traditional methods, demonstrating the advantages of change detection.

show abstract