Speckle Removal from SAR Images in the Lapped Transform Domain Using Adaptive Threshold Based on Despeckling Evaluation Indexes

Abstract Aiming at the source of underwater acoustic emission, in order to identify the enemy emission sonar source accurately. Using the digital watermarking technology and combining with the good time-frequency characteristics of fractional Fourier transform (FRFT), this paper proposes a sonar watermarking method based on fractional Fourier transform. The digital watermark embedding in the fractional Fourier transform domain and combined with the coefficient properties of the sonar signal in the fractional Fourier transform to select the appropriate watermark position. Using the different characteristics of the signals before and after embedding, an adaptive threshold was set for the watermark detection to realize the discrimination of sonar signals. The simulation results show the feasibility and has better resolution and large watermark capacity of this method, while the robustness of the watermark is better, and the detection precision is further improved.

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EXTRACTION OF EXTENDED SMALL-SCALE OBJECTS IN DIGITAL IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-5-w6-87-2015 ◽

2015 ◽

Vol XL-5/W6 ◽

pp. 87-93 ◽

Cited By ~ 12

Author(s):

V. Y. Volkov

Keyword(s):

Heavy Tails ◽

Speckle Noise ◽

Radio Observation ◽

Adaptive Threshold ◽

Binary Image ◽

Small Scale ◽

Sar Images ◽

Television Camera ◽

Directional Filtering ◽

Threshold Setting

Detection and localization problem of extended small-scale objects with different shapes appears in radio observation systems which use SAR, infra-red, lidar and television camera. Intensive non-stationary background is the main difficulty for processing. Other challenge is low quality of images, blobs, blurred boundaries; in addition SAR images suffer from a serious intrinsic speckle noise. Statistics of background is not normal, it has evident skewness and heavy tails in probability density, so it is hard to identify it. The problem of extraction small-scale objects is solved here on the basis of directional filtering, adaptive thresholding and morthological analysis. New kind of masks is used which are open-ended at one side so it is possible to extract ends of line segments with unknown length. An advanced method of dynamical adaptive threshold setting is investigated which is based on isolated fragments extraction after thresholding. Hierarchy of isolated fragments on binary image is proposed for the analysis of segmentation results. It includes small-scale objects with different shape, size and orientation. The method uses extraction of isolated fragments in binary image and counting points in these fragments. Number of points in extracted fragments is normalized to the total number of points for given threshold and is used as effectiveness of extraction for these fragments. New method for adaptive threshold setting and control maximises effectiveness of extraction. It has optimality properties for objects extraction in normal noise field and shows effective results for real SAR images.

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Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

AIP Advances ◽

10.1063/1.4998400 ◽

2018 ◽

Vol 8 (3) ◽

pp. 035002 ◽

Cited By ~ 2

Author(s):

Liangliang Li ◽

Yujuan Si ◽

Zhenhong Jia

Keyword(s):

Image Enhancement ◽

Adaptive Threshold ◽

Transform Domain ◽

Shearlet Transform ◽

Nonsubsampled Shearlet Transform

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Effective Transform Domain Denoising of Oceanographic SAR Images for Improved Target Characterization

Remote Sensing and Digital Image Processing - Artificial Intelligence Techniques for Satellite Image Analysis ◽

10.1007/978-3-030-24178-0_6 ◽

2019 ◽

pp. 107-136

Author(s):

S. Arivazhagan ◽

W. Sylvia Lilly Jebarani ◽

R. Newlin Shebiah ◽

S. Vineth Ligi ◽

P. V. Hareesh Kumar ◽

...

Keyword(s):

Transform Domain ◽

Sar Images ◽

Target Characterization

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Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition

Sensors ◽

10.3390/s18103293 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3293

Author(s):

Yu-Huan Zhao ◽

Peng Liu

Keyword(s):

Detection Method ◽

Adaptive Threshold ◽

Detection Task ◽

Synthetic Aperture ◽

Small Islands ◽

Constant False Alarm Rate ◽

Sar Images ◽

Ship Detection ◽

Clutter Reduction ◽

Variance Weighted Information Entropy

In this paper, we present an adaptive ship detection method for single-look complex synthetic aperture radar (SAR) images. First, noncircularity is analyzed and adopted in ship detection task; besides, similarity variance weighted information entropy (SVWIE) is proposed for clutter reduction and target enhancement. According to the analysis of scattering of SVWIE and noncircularity, SVWIE-noncircularity (SN) decomposition is developed. Based on the decomposition, two components, the high-noncircularity SVWIE amplitude (h) and the low-noncircularity SVWIE amplitude (l), are obtained. We demonstrate that ships and clutter in SAR images are different for h detector and h detector can be effectively used for ship detection. Finally, to extract ships from the background, the generalized Gamma distribution (G Γ D) is used to fit h statistics of clutter and the constant false alarm rate (CFAR) is utilized to choose an adaptive threshold. The performance of the proposed method is demonstrated on HH polarization of Alos-2 images. Experimental results show that the proposed method can accurately detect ships in complex background, i.e., ships are close to small islands or with strong noise.

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