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It is usually difficult but important to extract distant targets from sea clutters and clouds since the targets are small compared to the pixel field of view. In this paper, an algorithm based on wavelet transformation is proposed for automatic detection of small targets under the maritime background. We recognize that the distant small targets generally appear near the sea-sky horizon line and noises lie along the direction of sea-sky horizon line. So the sea-sky horizon is located firstly by examining the approximate image of a Haar wavelet decomposition of the original image. And the equation of the sea-sky horizon is set up, no matter whether the sea-sky horizon is horizontal or not. Since the sea-sky horizon is located, not only the potential area but also the strip direction of noise is got. Then the modified mutual wavelet energy combination algorithm is applied to extract targets with targets being marked by red windows. Computer simulations are shown to validate the great adaptability of the sea-sky horizon line detection and the accuracy of the small targets detection. The algorithm should be useful to engineers and scientists to design precise guidance or maritime monitoring system. [ABSTRACT FROM AUTHOR]

• We proposed the small target and edge enhancement algorithm based on Encode-Decode for thermal image semantic segmentation. and it is improved by about 2.2% compared with the current thermal image segmentation algorithm. • We designed MPAM module. It is able to makes full use of the feature and context information of each layer to effectively recover details at pixel space. • We designed the EEM and STEM module to recover the detail information such as edges and small targets by explicitly modeling. • To maintain the semantic information of the edge features and small target features, we designed the specific functions to supervise them. The accuracy of the small target features and edges features with semantic category information can be improved. The thermal image segmentation has been widely concerned to alleviate the limitations of image segmentation caused by imaging of visible spectrum under challenge environmental conditions. The existing thermal image segmentation methods are not easy to obtain high quality segmented results, because thermal images are lack of color information, edges are unclear, details are not obvious. To this end, we proposed a multi-level pixel spatial attention network for thermal image segmentation. Specifically, we designed a pixel space attention module on each layer of the backbone network(MPAM), which recovers the more spatial detailed and maintains more semantic information. Then, we designed an edge extraction module (EEM) and a small target extraction module (STEM), which enhances the edge and small target features of the network by explicitly modeling the edge and small target features. Finally, the edge and small target features were fused with the output features of backbone, and the specialized loss functions was used to supervise them. Experimental results on SCUT-SEG, SODA and STI-Cityscpaes dataset demonstrate that our approach is slightly improved by 2.2 % compared with other the state-of-art algorithms in the same scene. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce an edge directional 2D least mean squares (LMSs) filter for small target detection in infrared (IR) images. Generally, the 2D LMS filter functions as a background prediction to apply to IR small target detection field. In order to accurately predict background objects as well as regions covered by small targets, the proposed 2D LMS filter take full advantage of edge information of prediction pixels corresponding to surrounding blocks around current filter window. And, to adjust adaptively its step size in the background and small target region, the adaptive region-dependent nonlinear step size is calculated by using the variance of the prediction pixels of the surrounding blocks. This prediction structure and adaptive step size of the proposed 2D LMS filter is applied to the background region including objects such as cloud edge and small target region differently. Through this way, the proposed 2D LMS filter predicts the background excluding small targets. Then, by subtracting the predicted background from the original IR image, small targets can be extracted. Experimental results show that the proposed 2D LMS filter has stronger target extraction and better background suppression ability compared to the existing 2D LMS filters. [Copyright &y& Elsevier]

• Edge features and local internal-external intensity features were analyzed. • Directional subbands were conducted to enhance the target and suppresses the interference through multiplying. • Multiscale candidate targets descriptor was designed to detect targets. In infrared maritime images, small targets are always dim, and there are interferences such as iands and wave clutter. Thus it is easy to miss the real target or generate a large number of false alarms. This paper proposes a detection method based on edge and local intensity features to improve detection accuracy. According to the difference in the similarity of directional edge components between targets and interference, constructing and multiplying directional subbands reduces false alarms. According to the local internal-external intensity features, we designed the candidate target descriptor. The proposed method has higher detection accuracy than several state-of-the-art methods and has stronger parameter robustness through experiments on images with different backgrounds and targets. [ABSTRACT FROM AUTHOR]