Abstract
Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of remote sensing. Conventional algorithms can fail in detecting small targets due to the low signal-to-noise ratios of the images. To solve this problem, an effective infrared small target detection algorithm inspired by random walks is presented in this paper. The novelty of our contribution involves the combination of the local contrast feature and the global uniqueness of the small targets. Firstly, the original pixel-wise image is transformed into an multi-dimensional image with respect to the local contrast measure. Secondly, a reconstructed seeds selection map (SSM) is generated based on the multi-dimensional image. Then, an adaptive seeds selection method is proposed to automatically select the foreground seeds potentially placed in the areas of the small targets in the SSM. After that, a confidence map is constructed using a modified random walks (MRW) algorithm to represent the global uniqueness of the small targets. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in both target enhancement and detection performance.
Highlights
Infrared search and tracking (IRST) systems have been widely applied in remote sensing, space surveillance, external intrusion warnings [1,2,3], etc
Some representative and advanced methods were chosen as baseline methods, such as the Top-Hat transformation-based (THT) method [20], the multiscale patch-based contrast measure-based (MPCM) method [52], the non-negative infrared patch-image model based on partial sum minimization of singular values-based (NIPPS) method [37], the gradient direction diversity weighted multiscale flux density-based (GDD-MFD) method [6] and the local steering kernel (LSK) reconstruction-based method [39]
It is important that small target detection algorithms are robust and efficient to meet the requirements of early warning systems
Summary
Infrared search and tracking (IRST) systems have been widely applied in remote sensing, space surveillance, external intrusion warnings [1,2,3], etc. For early warning applications, incoming targets such as missiles, aircraft, and boats are supposed to be detected at a long distance. Due to the remotely sensed imaging, the targets in an infrared image are of small size with a lack of prior knowledge about the target shape and texture features [4,5]. The targets are usually buried in heavy noise and complicated background clutter (e.g., irregular sunlit spot, sky–sea background, and heavy cloud). Small target detection under heavy noise and complex background is considered to be a difficult and challenging problem. Many research efforts have been made in this task over the past decades [7,8,9], it remains an open issue
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