&lt;title&gt;Data fusion and processing for airborne multichannel system of radar remote sensing: methodology, stages, and algorithms&lt;/title&gt;

Astola, Jaakko; Melnik, Vladimir P.; Kurekin, Andrei A.; Zelensky, Alexander A.; Kulemin, Gennady P.; Ponomarenko, Nikolay N.; Dolia, Alexander N.; Parkkinen, Jussi

doi:10.1117/12.381634

Cited by 7 publications

(8 citation statements)

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“…Similarly to the data presented in [11,19], Aiazzi et al underlines that small local variations of true image values that correspond to radar cross-section variations induced by local heterogeneity of surface backscattering should be preserved after image filtering; and these variations can be considered as texture.…”

Section: Introductionmentioning

confidence: 90%

“…For optical and infrared images it is commonly assumed that the dominant noise is additive and its probability density function (pdf) is close to Gaussian [8]. For microwave radar imagery the prevailing influence of multiplicative noise is typical and its pdf can be either close to Gaussian or essentially non-Gaussian depending upon the radar type and its characteristics [2,5,9,10,11]. This noise is commonly rather intensive and clearly observed visually in microwave images.…”

Section: Introductionmentioning

confidence: 99%

“…It serves the goals of image enhancement, denoising and, as a result, improvement of data interpretation, classification, segmentation, and sensed terrain (scene) parameter estimation [9,10,11]. Although a large number of books and papers deal with image filtering, not many of them pay particular attention to texture preservation.…”

Section: Introductionmentioning

confidence: 99%

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Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing

Tsymbal¹,

Ponomarenko

Zelensky

et al. 2005

EURASIP J. Adv. Signal Process.

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Textural features are one of the most important types of useful information contained in images. In practice, these features are commonly masked by noise. Relatively little attention has been paid to texture preserving properties of noise attenuation methods. This stimulates solving the following tasks: (1) to analyze the texture preservation properties of various filters; and (2) to design image processing methods capable to preserve texture features well and to effectively reduce noise. This paper deals with examining texture feature preserving properties of different filters. The study is performed for a set of texture samples and different noise variances. The locally adaptive three-state schemes are proposed for which texture is considered as a particular class. For "detection" of texture regions, several classifiers are proposed and analyzed. As shown, an appropriate trade-off of the designed filter properties is provided. This is demonstrated quantitatively for artificial test images and is confirmed visually for real-life images.

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Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing

Tsymbal¹,

Ponomarenko

Zelensky

et al. 2005

EURASIP J. Adv. Signal Process.

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“…The text representation of the designed LAF is the following: (1, 1, 0.0000, 2, 3), (2, 2, 0.0030, 4, 5), (4, 2, 0.0027, 6, 7), (6, 3, 0.0000, 8, 9), (8, 1), (9, 6), (7, 1), (5, 4, 0.0000, 10, 11), (10, 1), (11, 4, 0.0768, 12, 13), (12,1), (13,4), (3, 5, 0.2308, 14, 15), (14, 1, 0.4588, 16, 17), ( 16, 2, 0.0094, 18, 19), (18, 1, 0.1413, 20, 21 There are 10 used LAIs (18 thresholds) and 7 filters. The filters are enumerated as: 1 -DCT-based filter [14]; 2 -Idempotent filter; 3 -5x5 Local statistic Lee filter [7]; 4 -5x5 Modified sigma filter [15]; 5 -FIR-median hybrid filter 3LH+ [16]; 6 -5x5 Median filter; 7 -5x5 Standard sigma filter [17].…”

Section: Filtering For Better Object Detection In Imagesmentioning

confidence: 97%

“…Often noise presence considerably prevents reliable interpretation and classification of images, in particular, accurate estimation of sensed terrain parameters [4]. Thus, image denoising is a key stage in image processing [4].…”

Section: Introductionmentioning

confidence: 99%

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

Ponomarenko¹,

Zelensky²,

Astola

et al.

2006 IEEE Southwest Symposium on Image Analysis and Interpretation

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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.

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Influence of multiplicative noise variance evaluation accuracy on mm-band SLAR image filtering efficiency

Abramov¹,

Lukin²,

Ponomarenko³

et al.

The Fifth International Kharkov Symposium on Physics and Engineering of Microwaves, Millimeter, and Submillimeter Waves (IEEE C

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

Cited by 7 publications

References 0 publications

Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing

Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing

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

Influence of multiplicative noise variance evaluation accuracy on mm-band SLAR image filtering efficiency

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