Alexander A. Zelensky

Digital adaptive robust algorithms for radar image filtering

Novel adaptive robust filtering algorithms applicable to radar image processing are proposed. They take into consideration the peculiarities of radar images and possess a good combination of properties: effective speckle suppression, impulsive noise removal, edge and detail preservation and low computational complexity. The advantages of these digital algorithms are demonstrated by simulated data and images obtained by airborne side-look non SAR radar.

Improved minimal inter-quantile distance method for blind estimation of noise variance in images

Multichannel (multi and hyperspectral, dual and multipolarization, multitemporal) remote sensing (RS) is widely used in different applications. Noise is one of the basic factors that deteriorates RS data quality and prevents retrieval of useful information. Because of this, image pre-filtering is a typical stage of multichannel RS data pre-processing. Most efficient modern filters and other image processing techniques employ a priori information on noise type and its statistical characteristics like variance. Thus, there is an obvious need in automatic (blind) techniques for determination of noise type and its characteristics. Although several such techniques have been already developed, not all of them are able to perform appropriately in cases when considered images contain a large percentage of texture regions and other locally active areas. Recently we have designed a method of blind determination of noise variance based on minimal inter-quantile distance. However, it occurred that its accuracy could be further improved. In this paper we describe and analyze several ways to do this. One opportunity deals with better approximation of inter-quantile distance curve. Another opportunity concerns the use of image pre-segmentation before forming an initial set of local estimates of noise variance. Both ways are studied for model data and test images. Numerical simulation results confirm improvement of estimate accuracy for the proposed approach.

Local activity indicators for hard-switching adaptive filtering of images with mixed noise

We present a quantitative analysis of several local activity indicator properties and provide comparisons between them. We illustrate that the considered local activity indicators can be applied as adaptation parameters to locally adaptive hard-switching processing (filtering) of images. The cases where images are corrupted with additive or multiplicative noise having different probability density functions and, possibly, spikes are considered. The advantages of the designed adaptive hard-switching filtering algorithms are discussed and demonstrated for simulated data. Recommendations concerning the selection of local activity indicators and threshold values are given.

Quasi-optimal compression of noisy optical and radar images

It is often necessary to compress remote sensing (RS) data such as optical or radar images. This is needed for transmitting them via communication channels from satellites and/or for storing in databases for later analysis of, for instance, scene temporal changes. Such images are generally corrupted by noise and this factor should be taken into account while selecting a data compression method and its characteristics, in the particular, compression ratio (CR). In opposite to the case of data transmission via communication channel when the channel capacity can be the crucial factor in selecting the CR, in the case of archiving original remote sensing images the CR can be selected using different criteria. The basic requirement could be to provide such a quality of the compressed images that will be appropriate for further use (interpreting) the images after decompression. In this paper we propose a blind approach to quasi-optimal compression of noisy optical and side look aperture radar images. It presumes that noise variance is either known a priori or pre-estimated using the corresponding automatic tools. Then, it is shown that it is possible (in an automatic manner) to set such a CR that produces an efficient noise reduction in the original images same time introducing minimal distortions to remote sensing data at compression stage. For radar images, it is desirable to apply a homomorphic transform before compression and the corresponding inverse transform after decompression. Real life examples confirming the efficiency of the proposed approach are presented.

Data fusion and processing for airborne multichannel system of radar remote sensing: methodology, stages, and algorithms

Methodology and stages of data processing in multichannel airborne radar imaging systems are considered. It is shown that data fusion in such systems requires special techniques, algorithms, and software for image processing and information retrieval. Some approaches and methods are proposed. The results are demonstrated for simulated and real images.

Use of minimal inter-quantile distance estimation in image processing

Nowadays multichannel (multi and hyperspectral) remote sensing (RS) is widely used in different areas. One of the basic factors that can deteriorate original image quality and prevent retrieval of useful information from RS data is noise. Thus, image filtering is a typical stage of multichannel image pre-processing. Among known filters, the most efficient ones commonly require a priori information concerning noise type and its statistical characteristics. This explains a great need in automatic (blind) methods for determination of noise type and its characteristics. Several such methods already exist, but majority of them do not perform appropriately well if analyzed images contain a large percentage of texture regions, details and edges. Besides, many blind methods are multistage where some preliminary and appropriately accurate estimate of noise variance is required for next stages. To get around aforementioned shortcomings, below we propose a new method based on using inter-quantile distance and its minimization for obtaining appropriately accurate estimates of noise variance. It is shown that mathematically this task can be formulated as finding a mode of contaminated asymmetric distribution. And this task can be met for other applications. The efficiency of the proposed method is studied for a wide set of model distribution parameters. Numerical simulation results that confirm applicability of the proposed approach are presented. They also allow evaluating the designed method accuracy. Recommendations on method parameter selection are given.

Compression and classification of noisy multichannel remote sensing images

Remote sensing images are commonly formed on-board an observation platform, then transferred via a communication downlink, and finally processed on-land. There are many ways of compressing and then classifying remote sensing images. In this paper we focus on considering two lossy compression techniques under the assumption that the original images are noisy. No pre- or postprocessing is applied. Two classifiers are examined, namely, those based on trained radial basis function neural networks and support vector machines. We study how the parameter that controls the compression ratio of two coders based on the discrete cosine transform influences classification accuracy of these classifiers for a real life three-channel optical image. It is shown that attaining the optimal operation point for both coders is practically equivalent to providing the maximal probability of correct classification of multichannel data. At the same time, the efficiency of image compression characterized in terms of compression ratio, peak signal-to-noise ratio, and probability of correct classification considerably depends upon the coder used. Finally, it is shown that compressing multichannel remote sensing data in the neighborhood of the optimal operation point and near the maximum of the probability of correct classification can be performed in automatic manner.

Automatic Design of Locally Adaptive Filters for Pre-processing of Images Subject to Further Interpretation

Locally adaptive filters are widely used in image processing applications. However, their design commonly requires sufficient efforts and does not take into consideration some important aspects of further processing (interpreting and/or classification) of images. This paper puts forward a novel approach to automatic design of locally adaptive filters subject to further interpretation, namely, detection and localization of small size objects. Design is based on learning with clustering for a test image corrupted by a noise with statistical characteristics observed in real life images to which the obtained filter intend to be further applied. Quantitative data confirming the designed filter efficiency are presented

Locally adaptive image filtering based on learning with clustering

Image filtering or denoising is a problem widely addressed in optical, infrared and radar remote sensing data processing. Although a large number of methods for image denoising exist, the choice of a proper, efficient filter is still a difficult problem and requires wide a priori knowledge. Locally adaptive filtering of images is an approach that has been widely investigated and exploited during recent 15 years. It has demonstrated a great potential. However, there are still some problems in design of locally adaptive filters that is generally too heuristic. This paper puts forward a new approach to get around this shortcoming. It deals with using learning with clustering in order to make the procedure of locally adaptive filter design more automatic and less subjective. The performance of this approach to learning and locally adaptive filtering has been tested for mixed Gaussian multiplicative+impulse noise environment. Its advantages in comparison to another learning methods and the efficiency of the considered component filters is demonstrated by both numerical simulation data and real-life radar image processing examples.

Adaptive-vector LQ filter for color image processing

Robust adaptive vector filtering algorithms applicable to color and multichannel image processing are proposed. They are based on the use of Q-parameter that is a vector analog of quasirange. Considered algorithms have a good combination of properties: effective noise reduction, ability to remove spikes, edge and detail preservation, and low computational complexity. Their characteristics are evaluated quantitatively and compared to non-adaptive counterparts. Advantages of proposed algorithms are also demonstrated by simulated image processing results.