Noise SAR Research Articles

Lognormal Random Field models to identify temporal Land cover changes using full polarimetric L-Band SAR imagery

Multiplicative Autoregressive Random Field (MAR) based texture models have been identified as one of the most appropriate models for SAR intensity images to capture the stochastic spatial interaction among neighboring pixels. But very few studies have tested their viability particularly in disaster applications. In this paper, we analyse the MAR texture models for their advantageous in land cover change detection compared to the changes resulting from logarithm of SAR image intensity and speckle filtered SAR imagery. The paper shows that lognormal random fields with multiplicative spatial interactions in the form of MAR models can be an effective alternative to suppress speckle noise and model SAR image intensity in time series data analysis. The pre and post disaster observational data of the Tohoku earthquake, in the east coast of Japan, acquired by the Advanced Land Observation Satellite (ALOS)/phased array type L-band synthetic aperture radar (PALSAR) were synthesized using MAR model based texture measures. Two of the main texture descriptors of the MAR model were considered primarily in this study. Those are the neighborhood weighting and the noise variance parameters. A 2nd order neighborhood configuration was used to estimate them. We present a variogram based analysis, structural similarity index measure (SSIM), and the mean ratio detector (MRD) as three different approaches to analyse the changes in land cover using radar texture. The change detection results of the MRD were further tested using area error proportion (AEP), root mean square error (RMSE) and correlation coefficient (CC), keeping normalized ratio, principle component analysis (PCA) and adaptive Lee filtered polarimetric intensity based change as the references.

A statistical and Wiener filtering algorithm based on the curvelet transform for SAR images

ABSTRACTSynthetic aperture radar (SAR) images contain many kinds of noise. Speckle noise is multiplicative noise generated by the coherent imaging processes involved in SAR images and brings a great hindrance to the interpretation and application of SAR images, so it is considered the first major kind of noise in SAR images. SAR images also contain other incoherent additive noises generated by other factors, such as Gaussian noise, which are all considered the second major kind of noise. In order to reduce the impact of noise as much as possible, after an in-depth study of SAR imaging and noise-generating mechanism, curvelet transform principle, and Wiener filtering characteristic, a novel filtering method, here called the statistical and Wiener based on curvelet transform (SWCT) method is proposed. The SWCT algorithm processes two different kinds noise based on their properties. Specifically, it establishes a two-tiered filtering framework. For the first kind of noise, the algorithm uses the curvelet transform to decompose the SAR image and uses the statistical characteristics of the SAR image to generate an adaptive filtering threshold of the coefficients of decomposition to recover the original image. Then it filters every sub-band image at each decomposed scale and performs the inverse curvelet transform. The second kind of noise is directly filtered using the Wiener filter in the SWCT algorithm. Using the two-tiered filtering model and fully exploiting statistical characteristics, the SWCT algorithm not only reduces the amount of coherent speckle noise and incoherent noise effectively but also retains the edges and geometric details of the original SAR image. This is very good for target detection, classification, and recognition. Qualitative and quantitative tests were performed using simulated speckle noise, Gaussian noise, and real SAR images. The proposed SWCT algorithm was found to remove noise effectively and the performance of the algorithm was tested and compared to the mean filter, enhanced gamma-MAP (maximum a posterior probability) filter, wavelet transform filter, Wiener filter, and curvelet transform filter. Experiments carried out on real SAR images confirmed that the new method has a good filtering effect and can be used on different SAR images.

Fusion of Images Generated by Radiometric and Active Noise SAR

Abstract The work is devoted to fusion of radar and radiometer images. Noise waveform SAR generates radar images of reflective objects of its field of view. A bistatic radiometer with synthetic aperture estimates the thermal radio emissions of the objects along their angular coordinates and even range. The estimated brightness temperatures of rough and smooth surfaces are different, as well as the radar responses from them. Identification of the parameters of objects surfaces may be done using results of joint processing of images generated by both sensors. The optimum and quasi-optimum criteria for fusion of the images were obtained. The latter was experimentally checked. It approves the opportunity to fuse the images for further estimation of some parameters of objects surfaces. The results obtained may be used in environmental and security applications.

Nonuniform Spatial Sampling in a Ground-Based Noise SAR

Nonuniform Spatial Sampling in a Ground-Based Noise SARThe paper presents an idea of nonuniform spatial sampling applied to a noise synthetic aperture radar. In certain cases it is desirable to limit the number of spatial (along-track) domain samples acquired in a SAR radar because of external constraints on sampling frequency or on the overall number of samples - e.g. in order to economy on time or power consumed. Lowering number of samples taken may, however, lead to spatial aliasing and incorrect reconstruction of the image. Nonuniform sampling allows to reduce the aliasing effect and reconstruct the image better. This technique can be applied with standard reconstruction methods, but it works best together with Compressive Sensing reconstruction algorithms. The idea will be verified with an experimental noise SAR built at ISE PW.

Pseudo-Noise Waveform Design Minimizing Range and Doppler Masking Effect

Noise radar is a very promising technology offering advantages which cannot be achieved in classical radar systems. Unfortunately, it has also several disadvantages. One of the most significant is the masking effect, where strong objects raise the noise floor preventing detection of weak ones and significantly affecting dynamic range. In this article method of designing waveform with reduced noise floor is presented. Keywords—Noise radar, noise SAR, waveform design.

Imaging with a Noise SAR in the Near-Field Zone of the Source

Imaging with a Noise SAR in the Near-Field Zone of the Source