Design of New Wavelet Packets Adapted to High-Resolution SAR Images With an Application to Target Detection

Abstract

High-Resolution in synthetic aperture radar (SAR) leads to new physical characterizations of scatterers which are anisotropic and dispersive. These behaviors present an interesting source of diversity for target detection schemes. Unfortunately, such characteristics have been integrated and have been naturally lost in monovariate single look SAR images. Modeling this behavior as non-stationarity, wavelet analysis has been successful in retrieving this information. However, the sharp-edge of the used wavelet functions introduces undesired high side-lobes for the strong scatterers present in the images. In this paper, a new family of parametrized wavelets, designed specifically to reduce those side-lobes in the SAR image decomposition, is proposed. Target detection schemes are then explored using this spectroangular diversity and it can be shown that in High-Resolution SAR images, the non-Gaussian and robust framework leads to better results.

Robust Low-Rank Change Detection for Multivariate SAR Image Time Series

“…Each pixel of a SAR image at a given date t corresponds to a vector of dimension p, denoted x ∈ C p . The p channels can correspond to a polarimetric diversity (p = 3), or to another kind of diversity such as a spectro-angular one, obtained through wavelet transforms [5]. The change detection process is applied using a local window around the pixel of interest, including K pixels.…”

“…Since the scatterers present in this scene exhibit an interesting spectro-angular behavior, each polarization of these images has been subjected to the wavelet transform presented in [5], allowing to obtain images of dimension p = 12.…”

“…Among popular methodologies, Coherent Change Detection (CCD) [3] and the log-ratio operator [4] have received noticeable attention. However, these methodologies are limited to pairs of one-dimensional images, while current data-sets are generally multidimensional (using e.g., polarimetric or spectro-angular channels [5]), and gather multiple observation-times of the same scene. Multivariatedata oriented methodologies, i.e.…”

Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method

“…With the advent of studies on scaling properties, the use of the texture transform method based on time-frequency domain (TFD) techniques has become popular for the purpose of texture analysis [27]- [32]. Moreover, discrete wavelet transform (DWT) applications for SAR image processing have received much attention in the literature in contrast to other TFD techniques [33]- [36]. With the help of the DWT algorithm, SAR images can not only be transformed into both spatial and frequency domain information as a multiresolution technique but can also be decomposed into further scales, which is referred to as multiscale implementation.…”

“…With the help of the DWT algorithm, SAR images can not only be transformed into both spatial and frequency domain information as a multiresolution technique but can also be decomposed into further scales, which is referred to as multiscale implementation. Accordingly, a TFD fractal formulation of the SAR sea-ice texture in the presence of embedded HMN and pixel anomalies based on DWT application can be considered a novel SAR sea-ice fractal research study that has not yet been performed on sea-ice textures [4], [7], [9]- [13], [18]- [23], [25]- [36]. In other words, multiresolution DWT decomposition is not only helpful to investigate the adverse effects of the presence of HMN and its suppression within the SAR sea-ice texture but also assists the multiscale probability distribution formulation of noncoherent structures and classification along with preservation of the fine details of the sea-ice.…”