Régis C. P. Marques

SAR imagery segmentation by statistical region growing and hierarchical merging

This paper presents an algorithm to segment synthetic aperture radar (SAR) images, corrupted by speckle noise. Most standard segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, i.e. without any preprocessing step. The algorithm includes a statistical region growing procedure combined with hierarchical region merging. The region growing step oversegments the input radar image, thus enabling region aggregation by employing a combination of the Kolmogorov-Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for performance improvement. We have tested and assessed the proposed technique on artificially speckled image and real SAR data.

SAR Image Segmentation Based on Level Set Approach and {\cal G}_A^0 Model

This paper proposes an image segmentation method for synthetic aperture radar (SAR), exploring statistical properties of SAR data to characterize image regions. We consider {\cal G}_A^0 distribution parameters for SAR image segmentation, combined to the level set framework. The {\cal G}_A^0 distribution belongs to a class of {\cal G} distributions that have been successfully used to model different regions in amplitude SAR images for data modeling purpose. Such statistical data model is fundamental to deriving the energy functional to perform region mapping, which is input into our level set propagation numerical scheme that splits SAR images into homogeneous, heterogeneous, and extremely heterogeneous regions. Moreover, we introduce an assessment procedure based on stochastic distance and the {\cal G}_A^0 model to quantify the robustness and accuracy of our approach. Our results demonstrate the accuracy of the algorithms regarding experiments on synthetic and real SAR data.

Target Detection in SAR Images Based on a Level Set Approach

This paper introduces a new framework for point target detection in synthetic aperture radar (SAR) images. We focus on the task of locating reflective small regions using a level-set-based algorithm. Unlike most of the approaches in image segmentation, we address an algorithm that incorporates speckle statistics instead of empirical parameters and also discards speckle filtering. The curve evolves according to speckle statistics, initially propagating with a maximum upward velocity in homogeneous areas. Our approach is validated by a series of tests on synthetic and real SAR images and compared with three other segmentation algorithms, demonstrating that it configures a novel and efficient method for target-detection purpose.

Edge preserving wavelet speckle filtering

A new edge preserving wavelet filtering is proposed and it is applied to real SAR images that are generally affected by a multiplicative noise, called speckle, which degrades the quality of these images. The new approach attempts to look for the neighborhood area in the detail images of a wavelet decomposition, that identifies homogeneous areas and edge information by using masks in order to reduce speckle while edges are preserved The improved filtering method uses the Nagao and Matsuyama and Tomita and Tsuji masks to detect edge locations in wavelet subspaces. The information provided by the masks is used to distinguish which of the detail coefficients, are to be shrunk.

Locating oil spill in SAR images using wavelets and region growing

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

Filtering effects on SAR images segmentation

This paper evaluates filtering effects on SAR image segmentation testing four types of speckle reduction algorithms. A primary goal of these filters is to provide a large amount of speckle noise reduction in homogeneous areas and to preserve edges and details. To assess the effects produced by the filters in the posterior segmentation task, some quality measures are calculated from the processed images and used to indicate the filtering ability for features preservation.

Evaluating an adaptive windowing scheme in speckle noise MAP filtering

Synthetic aperture radar (SAR) images are corrupted by speckle noise, which degrades the quality and interpretation of the images. Speckle removal provides a better interpretability of SAR images if the technique performs the filtering without loss of spatial resolution and preserves fine details and edges. This work aims to redefine the neighborhood areas around the noisy pixel and in this area the local mean and variance are computed to estimate the Maximum a Posteriori (MAP) filter parameters. The proposed modified MAP algorithm improves the ability to filter the speckle noise without blurring edges and targets by applying the MAP estimator in the current adaptive window that is controlled by a measure of homogeneity in the area around the noisy pixel. This adaptive windowing was also incorporated to the classical Kuan et al. (1985) and Frost et al. (1982) filters in order to evaluate the performance of the proposed scheme. The effectiveness in reducing speckle by the modified MAP filter is evaluated in terms of qualitative and quantitative aspects such as line and edge preservation and the improvement of the signal to noise ratio. The tests were performed in real SAR images.

SAR Image Segmentation With Rényi's Entropy

Synthetic aperture radar (SAR) image segmentation is an important task in image processing. However, classic segmentation techniques are inadequate due to the presence of speckle noise. In this paper, we present a methodology for SAR image segmentation that uses the matrix of Rényis entropy. This matrix arises from SAR data that follows the GA0 model, and here, it is an input to segmentation methods. For performance evaluation of the proposed methodology, we employ the error of segmentation, the cross-region fitting index, the Dice measure, as well as the rates of false positives and negatives. Tests have been performed on synthetic and real SAR images and the matrix of entropy has improved the results, regardless of the increase of the number of looks. The Otsus method of global thresholding produced good segmentation results when applied to this matrix.

A Comparison of Filters for Ultrasound Images

The coherent nature of ultrasonic waves, that provides information for ultrasound image formation, results in the appearance of speckle noise. The development of speckle noise filtering methods for ultrasound B-scan images is very important for accurate detection of targets and boundaries. Many filters have been proposed in the literature for speckle reduction, most of them in remote sensing applications. This paper presents a comparison of filters for ultrasound images. The tests were performed in images artificially contaminated with speckle noise supposed to be Rayleigh distributed and a phantom ultrasound image.

Multiscale denoising algorithm based on the a trous algorithm

In this work we present a novel application of the multiscale denoising algorithm proposed by Sita and Ramakrishnan (2000). We used it to filter artificially contaminated images by multiplicative speckle and additive Gaussian noise, respectively. This filtering scheme is a combination of the shift invariant discrete wavelet and nonlinear filtering applied to evoked potential signals. It employs a redundant discrete wavelet (the a trous algorithm) removing the smallest wavelet coefficients in each dyadic scale guided by the correlation existing between them in different scales.