Feature-Enhanced Speckle Reduction via Low-Rank and Space-Angle Continuity for Circular SAR Target Recognition

Chen, Lin; Jiang, Xue; Zhou, Li; Liu, Xingzhao

doi:10.1109/tgrs.2020.2983420

Cited by 38 publications

(14 citation statements)

References 38 publications

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“…The ArcSAR configuration (i.e., radar antenna placed on a tripod which follows a circular movement) has solved many drawbacks of conventional TInSAR, such as large sizes, heavy weights, and significant on site infrastructure requirements [ 91 ]. Basically, ArcSAR allows to retrieve better resolution with smaller antenna [ 92 , 93 ].…”

Section: Methodsmentioning

confidence: 99%

Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring: The Poggio Baldi Landslide Natural Laboratory

Romeo

Cosentino

Giani

et al. 2021

Sensors

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Nowadays the use of remote monitoring sensors is a standard practice in landslide characterization and monitoring. In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment while limited studies and applications are still available for ArcSAR Interferometry, Gigapixel imaging and Acoustic sensing. Taking advantage of the facilities located at the Poggio Baldi Landslide Natural Laboratory, an intensive monitoring campaign was carried out on May 2019 using simultaneously the HYDRA-G ArcSAR for radar monitoring, the Gigapan robotic system equipped with a DSLR camera for photo-monitoring purposes and the DUO Smart Noise Monitor for acoustic measurements. The aim of this study was to evaluate the potential of each monitoring sensor and to investigate the ongoing gravitational processes at the Poggio Baldi landslide. Analysis of multi-temporal Gigapixel-images revealed the occurrence of 84 failures of various sizes between 14–17 May 2019. This allowed us to understand the short-term evolution of the rock cliff that is characterized by several impulsive rockfall events and continuous debris production. Radar displacement maps revealed a constant movement of the debris talus at the toe of the main rock scarp, while acoustic records proved the capability of this technique to identify rockfall events as well as their spectral content in a narrow range of frequencies between 200 Hz to 1000 Hz. This work demonstrates the great potential of the combined use of a variety of remote sensors to achieve high spatial and temporal resolution data in the field of landslide characterization and monitoring.

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Section: Methodsmentioning

confidence: 99%

Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring: The Poggio Baldi Landslide Natural Laboratory

Romeo

Cosentino

Giani

et al. 2021

Sensors

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“…There are many granular specks distributed on the image, which is called speckle noise. Speckle is commonly interpreted as a kind of locally correlated noise that reduces image contrast and conceals fine feature details, causing negative effects on target detection and recognition [2,3] scene segmentation [4], and image registration [5]. In consideration of the damaging effect of speckle on images, speckle suppression is required to smooth uniform areas of the images and preserve the features, like edges and textures.…”

Section: Introductionmentioning

confidence: 99%

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Wang

et al. 2022

Remote Sensing

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Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising models is proposed in this paper. Taking full account of the local statistics in the image, a quantization technique—which is different from the normal edge detection method—is supported by the variation coefficient of the image. Accordingly, a quantizer is designed to respond to both noise level and edge strength. This quantizer automatically determines the threshold of diffusion coefficient and controls the weight between total variation filter and anisotropic diffusion partial differential equation filter. A series of experiments are conducted to test the performance of the quantizer and proposed filter. Extensive experimental results have demonstrated the superiority of the proposed method with both synthetic images and natural SAR images.

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“…As one of the most popular application fields of SAR, SAR target recognition (Shi et al., 2021; Wang et al., 2021; Wen et al., 2019) has attracted increasing popularity nowadays and demonstrated great vitality in environmental monitoring, disaster relief, surveillance and reconnaissance areas. Plenty of new techniques have been adopted to realize SAR target recognition (Chen et al., 2020; Wang et al., 2020, 2021). Various advanced algorithms have been employed for SAR target recognition, such as sparse representation (SR) (He et al., 2020; Liu et al., 2018a; Ning et al., 2019; Zhou et al., 2019) and manifold learning (Dong et al., 2017; Huang et al., 2016; Liu et al., 2018b).…”

Section: Introductionmentioning

confidence: 99%

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

Liu

Chen

2022

Radio Science

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A multi‐manifold based sparse representation (MMSR) algorithm which can preserve the local structure of the datasets in the reconstruction space with multiple descriptions is proposed for synthetic aperture radar (SAR) target configuration recognition. Different from the traditional manifold learning‐based algorithms that preserve the local structure of the datasets in the low‐dimensional space, the proposed MMSR guarantees local structure preserving of the datasets after feature extraction and sparse representation. The proposed MMSR can avoid corrupted relationships of the samples to ease the obstacle of target aspect angle sensitivity, which can realize more accurate SAR target configuration recognition. Satisfying results are obtained on the moving and stationary target acquisition and recognition database.

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Feature-Enhanced Speckle Reduction via Low-Rank and Space-Angle Continuity for Circular SAR Target Recognition

Cited by 38 publications

References 38 publications

Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring: The Poggio Baldi Landslide Natural Laboratory

Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring: The Poggio Baldi Landslide Natural Laboratory

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

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