Wind-directional effects on the hydrodynamic modulation of microwave radar images of ocean waves

Abstract

A numerical simulation has been constructed of the hydrodynamic modulation transfer function (HMTF) which defines the spatial and spectral properties of swell waves as observed by an imaging radar. While the simulation is based on the Alpers and Hasselmann (1978) two-scale modulation transfer model, it explicitly takes account of directional properties of the short-wave spectrum and does not necessarily assume an isotropic wind-wave spectrum. Several different spreading functions are modelled and it is demonstrated that an anisotropic wind wave field significantly distorts the HMTF. Depending on the wind direction relative to the radar azimuth, the effect can be to shift the direction and the wave number of the peak of the image spectrum relative to the true swell spectrum. The effect of fetch limitation on the wind-wave spectrum is also examined based on the JONSWAP spectrum, but the consequences of this for the HMTF imaging mechanism are found to be minimal.     

Statistical investigations of the synthetic aperture radar images

The focused and non-focused image-forming methods for airborne synthetic aperture radar (SAR) are investigated. The more precise receiving signal model which includes not only traditional additive noise but the multiplicative noise which is the consequence of the aircraft's velocity and height fluctuations is developed. Results of numerical calculations of the root-mean-square (rms) errors of the set of principal image parameters as functions of the additive noise variance and multiplicative noise standard deviation for several weighting windows and the histograms of the distributions of some image parameters are presented.     

The effect of median filtering on synthetic aperture radar images

Synthetic aperture radar (SAR) images are subject to intrinsic 'noise', called speckle, over and above any spatial variability due to variations in the properties of the scene. Many noise-reduction techniques have been employed to reduce the effects of this phenomenon. In this note we review the statistical effects of one of the simplest such techniques, the median filter. This filter can be performed almost as rapidly as the mean (box average) filter but has significantly better edge-preserving properties. It is, however, unsuited to images containing significant point- or small-target features. Use of the median filter can introduce significant biases into the data, for example a 25 per cent reduction in an intensity image after 3 by 3 median filtering. This note presents calculations of the size of these biases for the case of homogeneous target areas, fully-developed speckle, and statistically independent looks in multi-look images.     

Surface expressions of bathymetry on SEASAT synthetic aperture radar images

Abstract

This paper identifies three classes of feature observed on SEASAT synthetic aperture radar images that may be related to changes in water depth. The first is refraction of gravity waves. The second is the modulation of the surface roughness as tidal currents flow over changes in the depth of shallow water. The third is the detection of internal waves propagating from steep gradients in deep water.     

Multiregion level-set partitioning of synthetic aperture radar images

The purpose of this study is to investigate synthetic aperture radar (SAR) image segmentation into a given but arbitrary number of gamma homogeneous regions via active contours and level sets. The segmentation of SAR images is a difficult problem due to the presence of speckle which can be modeled as strong, multiplicative noise. The proposed algorithm consists of evolving simple closed planar curves within an explicit correspondence between the interiors of curves and regions of segmentation to minimize a criterion containing a term of conformity of data to a speckle model of noise and a term of regularization. Results are shown on both synthetic and real images.     

基于信号特征的雷达图像无损压缩算法

提出了一个有效的在VDR中记录雷达图像的无损压缩算法。该算法基于对雷达信号特征的分析，充分挖掘其中的各种信息冗余进行压缩编码。首先进行帧间预测编码，并解决了可能出现的误差累积问题。在帧内预测编码时，提出了沿最小梯度方向进行预测的分类预测器。在熵编码阶段，提出了若干图像序列共享概率模型的方法。     

Signatures of resonant and non-resonant scattering mechanisms on radar images of internal waves

The results of two polarization airborne radar imagery tests of the ocean surface obtained during the JUSREX'92 experiment are presented. It is shown that the traditional composite surface model with small-scale 'Bragg' waves superposed over larger gravity waves can not explain either the contrasts of internal wave surface manifestations in conditions of a stable atmospheric boundary layer at low grazing angles (LGA), or the apparent difference between the images obtained at different polarizations in unstable atmospheric conditions. We attribute this discrepancy to the presence of mesoscale steep waves, which produce non-resonant scattering and make different relative contributions to the total cross sections for the two polarizations. The possibility of distinguishing between surface manifestations of atmospheric and oceanic origin is also discussed.     

Real-time analysis of ships in radar images with neural networks

This paper shows the effectiveness of using neural paradigms to filter, enhance and restore radar images of ships sailing in low visibility conditions. The task, to be accomplished in real time, is inserted in a processing chain which provides the mobile trajectory. Adaptive vector quantization techniques (supervised and unsupervised) and back-propagation neural algorithms are considered. The final optimized architecture contains a small set of processing modules comprising, at most, two pipelined neural convolvers (an enhancing filter and a classifier). Each neural-convolver mask is characterized by a symmetrical structure whose optimal coefficients have been determined via learning.     

Suppression of speckle in synthetic aperture radar images using wavelet

A filter for suppressing speckle in synthetic aperture radar (SAR) images utilizing wavelet is proposed. The filter suppresses speckle by reducing the amplitude of the detail images in wavelet subspaces, while preserving edges by releasing the amplitude reduction around edges; information on edges, contained in the detail images, is utilized for edge detection. Simulations and application to SAR images have shown that the performance of the filter is satisfactory in both smoothing and edge preservation, and in generating visually-natural images as well.     

Application of the deflection criterion to classification of radar SAR images

An original application of the weighted deflection is proposed for radar target classification by quadratic filters. An explicit formulation of optimal filters is derived. We analyze the impact of the weighting parameter on real data recognition and show that performances are better when the deflection coincides with the Fisher ratio.     

Criterions for detectability and strong detectability of faults in linear systems

A fault is (strongly) detectable if it is possible to construct a residual generator that is sensitive to the (constant) fault while decoupling all disturbances. Existing fault detectability criterions are reviewed and in two cases, improved versions are derived. For strong fault detectability, three new criterions are presented. To prove all criterions, a framework of polynomial bases is utilized. With these new and improved criterions, there exists now a criterion for models given both on transfer function form and state-space form, and for both fault detectability and strong fault detectability investigations. Recommendations are given on what criterion to use in different situations.     

The significance of lineaments mapped from remotely sensed images of the 1:250 000 Lau Sheet in the Benue trough of Nigeria

A lineament map of the 1:250000 Lau Sheet in the Benue trough of Nigeria is presented that is derived from LANDSAT and radar images. The two general trends of the lineaments correspond to the structural trend of the Nigerian basement rocks. New lineaments which are probably faults were delineated and recorded along with new circular features, There is a high probability of finding minerals of economic value in the study area, based on the lineament patterns and the tectonic history.     

Deep-learning-based extraction of the animal migration patterns from weather radar images

Continental coverage and year-round operation of the weather radar networks provide an unprecedented opportunity for studying large-scale airborne migration.The...     

Pattern recognition filtering and bidimensional FFT-based detection of storms in meteorological radar images

Important climate changes are today observed around the world, frequently yielding destructing precipitations. To identify and follow the evolution of storms in real-time, meteorological radar images collected in Setif (Algeria), Bordeaux (France), and Dakar (Senegal), are processed. Template and pattern recognition-based filters are firstly used to remove the ground clutter and keep the precipitation echoes unchanged. Bidimensional FFT is then applied to the filtered images, showing that the Fourier spectra characterising convective clouds differ significantly from those of stratified ones. This difference can be usefully employed by the radar operators to quickly detect the formation of violent storms. To forecast the growing of rainfall clouds and their motion, the related radar echoes are reconstituted using inverse FFT. It is found that the 26 first harmonics are sufficient to both rapidly and accurately reconstitute the surface of clouds whereas 82 distinct harmonics are needed to well reproduce their reflectivity.     

The use of segmentation and shape recognition techniques in synthetic aperture radar images

This paper discusses the use of shape recognition techniques in the context of Synthetic Aperture Radar (SAR) images. Results of a study using small bright targets in simulated imagery are presented.     

The interpretation of laser radar images by a knowledge-based system

This paper presents a knowledge-based system to interpret laser radar (ladar) images. The objective of this research is to detect and recognize man-made objects in outdoor scenes. Our system applies themultisensor fusion approach to multiple ladar modalities to improve both segmentation and interpretation. The segmentation modules are written in C. The knowledge-based interpretation system is constructed usingKEE and Lisp. Low-level attributes of image segments (regions) are computed by the segmentation modules and then converted to theKEE format. The interpretation system applies forward chaining in a bottom-up fashion to derive object-level interpretation from input generated by low-level processing and segmentation modules. The interpretation modules detect man-made objects from the background using low-level attributes. Segments are grouped into objects, which are then classified into predefined categories (vehicles, ground, etc.). The efficiency of the interpretation system is enhanced by transferring nonsymbolic processing tasks to a concurrent service manager (program). Experimental results using ladar data are presented.     

Analysis of radar images for rainfall forecasting using neural networks

This paper describes a new approach to the analysis of weather radar data for short-range rainfall forecasting based on a neural network model. This approach consists in extracting synthetic information from radar images using the approximation capabilities of multilayer neural networks. Each image in a sequence is approximated using a modified radial basis function network trained by a competitive mechanism. Prediction of the rain field evolution is performed by analysing and extrapolating the time series of weight values. This method has been compared to the conventional cross-correlation technique and the persistence method for three different rainfall events, showing significant improvement in 30 and 60 min ahead forecast accuracy.     

A technique for the identification and analysis of icebergs in synthetic aperture radar images of Antarctica

This paper describes an image analysis technique developed to identify icebergs depicted in synthetic aperture radar images of Antarctica and to determine the outlines of these icebergs. The technique uses a pixel bonding process to delineate the edges of the icebergs. It then separates them from the background water and sea ice by an edge-guided image segmentation process. Characteristics such as centroid position and iceberg area were calculated for each iceberg segment and placed in a file for input to appropriate statistical data analysis software. The technique has been tested on three ERS-1 SAR sub-images in which it succeeded in identifying virtually all segments containing icebergs of size six pixels or larger. The images were first passed through an averaging filter to reduce speckle. This process produced a pixel size of 100m x 100m. As implemented, the technique overestimates iceberg areas by about 20% on average and the detection rate falls off rapidly for icebergs less than six pixels in size. Performance in these areas is expected to improve when additional stages, based on a more detailed analysis of pixel intensity, are implemented.     

An adaptive filter for speckle suppression in synthetic aperture radar images

Synthetic aperture radar images are generally corrupted by speckle noise. This arises due to the coherent nature of radar echoes used in the image formation and it is often necessary to enhance the image by speckle suppression before data can be used in various applications. To suppress speckle and improve the radar image interpretability a simple filtering technique has been proposed. The filter is adaptive to the variance of pixel intensity in a sliding window and accordingly decides the number of nearest neighbours to the central pixel to replace its intensity with the average intensity of those nearest neighbours. The performance of the filter has been studied for speckle removal in the homogeneous areas and its edge retention capability and compared with some of the widely known speckle filters. The results show that the proposed filter retains edges, removes speckle noise and compares well with other known filters in the literature.     

Noise reduction in synthetic aperture radar images using fuzzy logic and genetic algorithm

Synthetic aperture radar (SAR) is a self-illuminating imaging technique; it produces high resolution images in all weather conditions, day and night. SAR images are widely accepted and used by many application scientists. However, the SAR images are corrupted with speckle noise. Speckle noises are caused by random interference of electromagnetic signals scattered by the object surface within one resolution element. The amount of noise and distribution of noise corrupting the image is unpredictable. Conventional noise filters are quantitative in nature; they are not well suited for uncertainty problems. Fuzzy logic is capable of handling uncertainty. In this work, noisy pixels in the images are identified by using fuzzy rules and filtered using fuzzy weighted mean, keeping the healthy pixels unchanged. The optimum value of parameters used in defining fuzzy membership function is determined by using genetic algorithm (GA). Reducing noise and simultaneously preserving image details are the two most desirable characteristics of noise filters. Peak signal-to-noise ratio (PSNR) and edge preserving factor (EPF) are used to evaluate the performance of the proposed fuzzy filter. SAR images affected by varying amounts of speckle noise are used to evaluate the performance. It was observed that the proposed filter suppresses noise and preserves image edges.