Infrared Small Target Detection Using Homogeneity-Weighted Local Contrast Measure

Du, Peng; Hamdulla, Askar

doi:10.1109/lgrs.2019.2922347

Cited by 66 publications

(30 citation statements)

References 15 publications

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“…Recent research considers the stages during and after estimation to achieve better performance and uses some weighting functions to further suppress clutters. For example, the weighted local difference measure (WLDM) [33][34][35] introduced the local entropy as a weighting function; the local self-similar (LSS) [36] used self-similarity as a weighting function; the homogeneity weighted local contrast measure (HWLCM) [37] used local homogeneity as a weighting function; the regional intensity level (RIL) [38] used the difference of RIL as a weighting function. However, the weighting function calculation is usually complex since the window size needs to be adjusted to deal with unknown size targets.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Han

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Background estimation is an efficient infrared (IR) small target detection method. However, to deal with unknown targets, the estimation window in existing algorithms should be adjusted to perform multiscale detection, and requires a lot of calculations. Besides, the stages during and after estimation have received wide attention in existing algorithms, but the research on the stages before estimation is insufficient. Moreover, existing algorithms typically regard the maximum value of different orientations as the estimation value. However, when a dim target is adjacent to high brightness background, it is easily submerged. This paper proposes a three-layer estimation window to detect targets of different sizes with only a single-scale calculation. The enhanced closest-mean background estimation method is then proposed and carefully designed before, during and after the estimation. Before estimation, the matched filter is adopted to improve the image signal-to-noise ratio. During estimation, the principle of closest-mean is proposed to suppress high brightness background. After estimation, a ratio-difference operation is performed to enhance the true target and suppress the background simultaneously. A simple checking mechanism is proposed to further improve the detection performance. Experiments on some IR images demonstrate the effectiveness and robustness of the proposed method. Compared with existing algorithms, the proposed method has better target enhancement, background suppression, and computational efficiency. Index Terms-IR small target, background estimation, matched filter, closest-mean, three-layer window.

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Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Han

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The main idea of these methods is to use the local information in the spatial domain to construct a saliency map, then the infrared targets are discriminated from background clutters by threshold segmentation on the saliency map. Han et al proposed IDoGb to enhance image contrast [16], and this method can remove low-frequency clutters but cannot effectively filter out noise and strong clutters in the high-frequency range [17]. Dong et al introduced an improved VAM which greatly improved the target saliency [18], but it was time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure

Yang

Dong

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Robust and effective detection of small target in infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this paper, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets' sparsity to separate real targets from background clutters. Firstly, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six data sets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.

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“…Infrared small target detection has been a hot topic for guidance, defense, navigation, infrared search and track and other photoelectric imaging systems [1][2][3][4]. In the past few decades several classical methods have been proposed, such as max-mean filter and max-median filter [5], Two-Dimensional Least Mean Square [6], bilateral filter [7], morphological filter [8].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

Xin

2020

Sensors

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Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.

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Infrared Small Target Detection Using Homogeneity-Weighted Local Contrast Measure

Cited by 66 publications

References 15 publications

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure

Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

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