Adaptive Detection of Subpixel Targets With Hypothesis Dependent Background Power

Golikov, Victor; Lebedeva, Olga

doi:10.1109/lsp.2013.2249662

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…In this paper, using the experimental data we investigate the relationship between two techniques of the detecting of a small low contrast floating objects on the sea surface in the image sequences: well known the MSD and recently proposed the MMSD [18]. Unlike previous studies, this work uses real images of the sea surface on which a model of a floating object is placed.…”

Section: Floating Object Signal Modelmentioning

confidence: 99%

“…Therefore, we do not use the model of reflections from the sea surface in this work, replacing it with real images of the agitated sea surface under various (3 types) weather conditions. We use a generic model of reflections from a floating object [18]. This approach allows, firstly, to avoid possible errors due to the inaccuracy of the model of reflections from the sea and, secondly, by introducing a model of a floating object, to control the magnitude of the contrast of the floating object relative to the sea surface.…”

Section: Floating Object Signal Modelmentioning

confidence: 99%

“…Detection algorithms based on statistical methods are widely used in remote sensing systems where channel noise and random intense background environment are present [13][14][15][16][17][18][19][20][21][22][23][24]. The well-known matched and matched subspace filters [15] are such algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Multi-pixel Low Contrast Object on a Real Sea Surface

Голиков¹,

Samovarov²,

Chernomorets³

et al. 2021

Preprint

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The video images captured at long range usually have low contrast floating objects of interest on a sea surface. A comparative experimental study of the statistical characteristics of reflections from floating objects and from the agitated sea surface showed the difference in the correlation and spectral characteristics of these reflections. The functioning of the recently proposed modified matched subspace detector (MMSD) is based on the separation of the observed data spectrum on two subspaces: relatively low and relatively high frequencies. In the literature the MMSD performance has been evaluated in generally and moreover using only a sea model (additive Gaussian background clutter). This paper extends the performance evaluating methodology for low contrast object detection and moreover using only the real sea dataset. This methodology assumes an object of low contrast if the mean and variance of the object and the surrounding background are the same. The paper assumes that the energy spectrum of the object and the sea are different. The paper investigates a scenario in which an artificially created model of a floating object with specified statistical parameters is placed on the surface of a real sea image. The paper compares the efficiency of the classical Matched Subspace Detector (MSD) and MMSD for detecting low-contrast objects on the sea surface. The article analyzes the dependence of the detection probability at a fixed false alarm probability on the difference between the statistical means and variances of a floating object and the surrounding sea.

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Section: Floating Object Signal Modelmentioning

confidence: 99%

Section: Floating Object Signal Modelmentioning

confidence: 99%

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Detection of Multi-pixel Low Contrast Object on a Real Sea Surface

Голиков¹,

Samovarov²,

Chernomorets³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the rather difficult tasks of detection on video frames is the objects detection on an agitated sea surface, which is due to the complex structure of the sea surface images on a separate frame, to the significant variability of the sea surface images on neighboring frames, as well as possible movements and distortions of the observed objects. To solve these tasks if there is information about changes in the pixels brightness of the objects and background images, methods based on matched subspace detectors [15,16] and their various modifications are used [17][18][19]. A method for detecting small objects floating on an agitated sea surface is given in [20].…”

Section: Introductionmentioning

confidence: 99%

Objects Detection Based on the Sea Surface Video Fragments Cross-Correlation

D.V.¹,

D.A.²,

E.V.³

et al. 2022

RR. I.T.

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The work is devoted to the development of a method for the objects detecting on an agitated sea surface video based on the analysis of the differences in the variability of the object and the sea surface image fragments on the neighboring frames. The proposed method does not use data about the object size, its shape, brightness, etc. The decision function has been developed that can be used to estimate the variability of a given frames fragment, based on the normalized cross-correlation coefficients values of the corresponding fragments on a video subsequent frames. The decision rule has been developed based on the proposed decision function, in which we use the threshold value (the critical domain boundary) determined at the training stage when analyzing the frames sequence fragments containing only the agitated sea surface image. The efficiency of the developed objects detection method on the agitated sea surface is demonstrated based on computational experiments. The values of the decision function critical domain boundary obtained at the training stage and the corresponding values of the type II error probability at the detection stage are given. The presented computational experiments results demonstrate that the developed method makes it possible to detect the object on video frames with the type II error probability equal to zero.

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Section: Introductionmentioning

confidence: 99%

Detection of Multi-Pixel Low Contrast Object on a Real Sea Surface

et al. 2022

Self Cite

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Video images captured at long range often show low-contrast floating objects of interest on a sea surface. A comparative experimental study of the statistical characteristics of reflections from floating objects and from the agitated sea surface showed differences in the correlation and spectral characteristics of these reflections. The functioning of the recently proposed modified matched subspace detector (MMSD) is based on the separation of the observed data spectrum on two subspaces: relatively low and relatively high frequencies. In the literature, the MMSD performance has been evaluated in general and using only a sea model (i.e., additive Gaussian background clutter). This paper extends the performance evaluating methodology for low contrast object detection using only a real sea dataset. The methodology assumes an object of low contrast if the mean and variance of the object and the surrounding background are the same. The paper assumes that the energy spectrum of the object and the sea are different. The paper investigates a scenario in which an artificially created model of a floating object with specified statistical parameters is placed on the surface of a real sea image. The paper compares the efficiency of the classical matched subspace detector (MSD) and MMSD for detecting low-contrast objects on the sea surface. The article analyzes the dependence of the detection probability at a fixed false alarm probability on the difference between the statistical means and variances of a floating object and the surrounding sea.

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Adaptive Detection of Subpixel Targets With Hypothesis Dependent Background Power

Cited by 12 publications

References 14 publications

Detection of Multi-pixel Low Contrast Object on a Real Sea Surface

Detection of Multi-pixel Low Contrast Object on a Real Sea Surface

Objects Detection Based on the Sea Surface Video Fragments Cross-Correlation

Detection of Multi-Pixel Low Contrast Object on a Real Sea Surface

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