Ensemble-Based Graph Model and Dense Reconstruction Error for Infrared Target Detection

In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.

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Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Remote Sensing ◽

10.3390/rs13204102 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4102

Author(s):

Genping Zhao ◽

Fei Li ◽

Xiuwei Zhang ◽

Kati Laakso ◽

Jonathan Cheung-Wai Chan

Keyword(s):

Anomaly Detection ◽

Sparse Representation ◽

Spectral Data ◽

Target Detection ◽

Reconstruction Error ◽

Spectral Unmixing ◽

Hyperspectral Images ◽

Background Signal ◽

Archetypal Analysis ◽

Target Signal

Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection.

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Selective Attention and Error Processing in an Illusory Conjunction Task

Journal of Psychophysiology ◽

10.1027/0269-8803.19.3.216 ◽

2005 ◽

Vol 19 (3) ◽

pp. 216-231 ◽

Cited By ~ 8

Author(s):

Albertus A. Wijers ◽

Maarten A.S. Boksem

Keyword(s):

Target Detection ◽

Event Related Potentials ◽

Illusory Conjunction ◽

Conjunction Error ◽

Error Related Negativity ◽

Display Element ◽

Related Potentials ◽

Feature Error ◽

High Level ◽

Correct Target

Abstract. We recorded event-related potentials in an illusory conjunction task, in which subjects were cued on each trial to search for a particular colored letter in a subsequently presented test array, consisting of three different letters in three different colors. In a proportion of trials the target letter was present and in other trials none of the relevant features were present. In still other trials one of the features (color or letter identity) were present or both features were present but not combined in the same display element. When relevant features were present this resulted in an early posterior selection negativity (SN) and a frontal selection positivity (FSP). When a target was presented, this resulted in a FSP that was enhanced after 250 ms as compared to when both relevant features were present but not combined in the same display element. This suggests that this effect reflects an extra process of attending to both features bound to the same object. There were no differences between the ERPs in feature error and conjunction error trials, contrary to the idea that these two types of errors are due to different (perceptual and attentional) mechanisms. The P300 in conjunction error trials was much reduced relative to the P300 in correct target detection trials. A similar, error-related negativity-like component was visible in the response-locked averages in correct target detection trials, in feature error trials, and in conjunction error trials. Dipole modeling of this component resulted in a source in a deep medial-frontal location. These results suggested that this type of task induces a high level of response conflict, in which decision-related processes may play a major role.

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