Sparsity-Driven Despeckling for SAR Images
Küçük Resim Yok
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the result of various SAR image processing tasks such as edge detection and segmentation. Thus, speckle reduction is critical and is used as a preprocessing step for smoothing homogeneous regions while preserving features such as edges and point scatterers. Although state-of-the-art methods provide better despeckling compared with conventional methods, their resource consumption is higher. In this letter, a sparsitydriven total-variation (TV) approach employing l0-norm, fractional norm, or l(1)-norm to smooth homogeneous regions with minimal degradation in edges and point scatterers is proposed. Proposed method, sparsity-driven despeckling (SDD), is capable of using different norms controlled by a single parameter and provides better or similar despeckling compared with the state-of-the-art methods with shorter execution times. Despeckling performance and execution time of the SDD are shown using synthetic and real-world SAR images.
Açıklama
Anahtar Kelimeler
Fractional norm, l(0)-norm, l(1)-norm, speckle reduction, synthetic aperture radar (SAR), total variation (TV)
Kaynak
Ieee Geoscience and Remote Sensing Letters
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
13
Sayı
1