THE EFFECTIVENESS OF THE RADON TRANSFORM AGAINST THE QUANTIZATION NOISE

The aim of image compression consists to reduce the number of bits required to represent an image. The Radon transform has become a very interesting tool in the field of image processing. Its Robustness against noises such as white noise has boosted the researchers to realize methods of detection of objects in noisy images. The Discrete Cosine Transform has shown its efficacy in the energy compaction of the image to be compressed into a smaller number of coefficients. It is part of many international standards including JPEG and MPEG. In this paper, we present an image compression method, which is the modification of the scheme presented by Predeep and Manavalan. The modification consists to use a high scale quantization, which is 20 in order to realize a heavy quantization for the DCT to achieve a high compression. A comparative study is performed to show the contribution of this modification

THE EFFECTIVENESS OF THE RADON TRANSFORM AGAINST THE QUANTIZATION NOISE

The aim of image compression consists to reduce the number of bits required to represent an image. The Radon transform has become a very interesting tool in the field of image processing. Its Robustness against noises such as white noise has boosted the researchers to realize methods of detection of objects in noisy images. The Discrete Cosine Transform has shown its efficacy in the energy compaction of the image to be compressed into a smaller number of coefficients. It is part of many international standards including JPEG and MPEG. In this paper, we present an image compression method, which is the modification of the scheme presented by Predeep and Manavalan. The modification consists to use a high scale quantization, which is 20 in order to realize a heavy quantization for the DCT to achieve a high compression. A comparative study is performed to show the contribution of this modification

Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation

Ship wake detection is of great importance in the characterisation of synthetic aperture radar (SAR) images of the ocean surface since wakes usually carry essential information about vessels. Most detection methods exploit the linear characteristics of the ship wakes and transform the lines in the spatial domain into bright or dark points in a transform domain, such as the Radon or Hough transforms. This paper proposes an innovative ship wake detection method based on sparse regularisation to obtain the Radon transform of the SAR image, in which the linear features are enhanced. The corresponding cost function utilizes the Cauchy prior, and on this basis, the Cauchy proximal operator is proposed. A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm (MYULA), which is computationally efficient and robust is used to estimate the image in the transform domain by minimizing the negative log-posterior distribution. The detection accuracy of the Cauchy prior based approach is 86.7%, which is demonstrated by experiments over six COSMO-SkyMed images.Comment: 9 pages, 2 Figures and 2 Table

Ship Wake Detection in SAR Images via Sparse Regularization

In order to analyse synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this paper, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e. the generalized minimax concave (GMC) function. Despite being a non-convex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L1, Lp, nuclear and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page

A False-alarm-controllable Modified AdaBoost Wake Detection Method Using SAR Images

A false-alarm-controllable modified AdaBoost-based method is proposed for detecting ship wake from sea clutter in synthetic aperture radar (SAR) images. It reformulates the wake detection problem as a binary classification task in the multifeature space. The update strategy of the sample weights in the original AdaBoost is modified for wake detection. First, a detection result confidence factor is designed to deal with class imbalance between sea clutter and ship wake; then, the AdaBoost is further modified as a false alarm rate (FAR) controllable detector by introducing penalty parameters to adjust weights update strategies for the sea clutter. Meanwhile, the multifeature space is spanned by a novel frequency peak height ratio (FPHA) feature and four salient features. FPHA is proposed to enhance the separation between the wake and sea clutter, which is computed from the amplitude spectrum peak of the image after the Fourier transform. Experimental results show that the proposed detector can tackle the imbalanced data problem and flexibly control FAR by adjusting penalty parameters. Moreover, improved detection probability is also achieved compared with existing methods