Dim and small infrared target fast detection guided by visual saliency

et al. 2020

IEEE Access

Infrared small target detection is a crucial and challenging topic for various applications. In recent years, the spectrum scale space (SSS) algorithm has shown considerable potential in the field of target detection. However, the SSS algorithm is prone to high false alarm rates in infrared small target detection scenarios with complex background. This paper proposes an improved SSS (ISSS) algorithm via precise feature matching and scale selection strategy for efficient infrared small target detection, which includes background suppression, feature matching and optimal scale selection three stages. In the background suppression stage, a matrix decomposition method named inexact augmented Lagrange multiplier (IALM) algorithm is used to extract the sparse image matrix from the original image as the target foreground image. In the feature matching stage, the 16 elaborate Gaussian kernel functions convolve with the the amplitude spectrum of target foreground image to generate 16 scale saliency maps that precisely match the feature of small targets. In the optimal scale selection stage, a few proper candidate scale maps are screened out according to the difference between the pixel values of the target area and the background clutters, in which the target area was more highlighted, and the scale map corresponding to the maximum value of local information entropy of the candidate saliency map is the final detection result map. We mainly made three contributions: First, IALM algorithm is utilized as a preprocessing step, and we have verified it is indispensable in eliminating most backgrounds with self-correlation property. Second, an elaborate scale division strategy is proposed to obtain multi-scale saliency maps that match the feature of infrared small targets precisely. Third, the gray value difference and the maximum value of local information entropy are defined and used as the judgment criteria for optimal scale selection. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art techniques, especially on infrared images with thick clouds and high-brightness buildings. INDEX TERMS Infrared small target detection, improved spectrum scale space, matrix decomposition.

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Scale Infrared Small Target Detection Method via Precise Feature Matching and Scale Selection Strategy

Yan

et al. 2020

IEEE Access

“…In the experiment, we select some state‐of‐the‐art methods as our baseline methods, which are novel weighted image entropy (NWIE) method [12 ], fast spectrum scale‐space and adaptive local contrast measure (TDGS is used for shorthand notation for method in paper [4 ]), Gaussian scale‐space and enhanced local contrast measure (GSS‐ELCM) method [10 ] and morphological feature and robust principal component analysis (MF‐RPCA) method [3 ]. Moreover, DWT method is also chosen as the baseline method since it is well studied.…”

Section: Methodsmentioning

confidence: 99%

“…Small infrared target detection is a key technology in infrared search and tracking systems [1–4 ], it has two basic ways, i.e. background suppression and target enhancement.…”

Section: Introductionmentioning

confidence: 99%

Small infrared target detection via multiscale kurtosis maps fusion

Wang

2020

Electronics Letters

In this Letter, a multiscale kurtosis maps fusion method is proposed for small infrared target detection. The authors have made two main contributions: first, they propose the scheme of using multiscale kurtosis maps to match the shape and the size of the small target; second, they present a dot product operation to achieve the fusion of multiscale kurtosis maps. Experimental results show that the presented method has a higher background suppression factor and signal‐to‐clutter ratio gain values than that of compared baseline methods, and it works effectively and more stably in complex backgrounds.

“…In [26], the authors attributed the poor detection performance of LCM to its lack of sufficient detailed analysis of the local environment. However, this is merely an indirect phenomenon.…”

Section: Motivationmentioning

confidence: 99%

Local contrast measure with iterative error for infrared small target detection

Yan

Zhang

2020

IET Image Processing

Local contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixelsized noises with high brightness (PNHB). Based on the analysis of the inherent causes of the poor performance of LCM in extremely complex backgrounds, this study presents an effective LCM with an iterative error. The contribution is as follows: first, the two-dimensional least mean square (TDLMS) filter with an adaptive parameter is applied to suppress the background clutters roughly in each multiscale window. Then, the partial maximum pixel mean is applied to the LCM to optimise the subblock statistical parameters, which achieves excellent strong edges suppression performance. Finally, the iteration error generated by TDLMS and the sub-block weight matrix is updated alternately to further optimise the statistical parameters of the contrast measure to make it more effective in suppressing PNHB. Experimental results demonstrate that the proposed approach is not only superior to the contrast methods in terms of high detection efficiency and low false alarm rate but also has satisfactory adaptability under extremely complex backgrounds.