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In this paper, we propose a novel method developed for detecting incomplete ship targets under cloud interference and low-contrast ship targets in thin fog based on superpixel segmentation, and outline its application to optical remote sensing images. The detection of ship targets often requires the target to be complete, and the overall features of the ship are used for detection and recognition. When the ship target is obscured by clouds, or the contrast between the ship target and the sea-clutter background is low, there may be incomplete targets, which reduce the effectiveness of recognition. Here, we propose a new method combining constant false alarm rate (CFAR) and superpixel segmentation with feature points (SFCFAR) to solve the above problems. Our newly developed SFCFAR utilizes superpixel segmentation to divide large scenes into many small regions which include target regions and background regions. In remote sensing images, the target occupies a small proportion of pixels in the entire image. In our method, we use superpixel segmentation to divide remote sensing images into meaningful blocks. The target regions are identified using the characteristics of clusters of ship texture features and the texture differences between the target and background regions. This step not only detects the ship target quickly, but also detects ships with low contrast and under cloud cover. In optical remote sensing, ships at sea under thin clouds are not common in practice, and the sample size generated is relatively small, so this problem is not applicable to deep learning algorithms for training, while the SFCFAR algorithm does not require data training to complete the detection task. Experiments show that the proposed SFCFAR algorithm enhances the detection of obscured ship targets under clouds and low-contrast targets in thin fog, compared with traditional target detection methods and as deep learning algorithms, further complementing existing ship detection methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method developed for detecting incomplete ship targets under cloud interference and low-contrast ship targets in thin fog based on superpixel segmentation, and outline its application to optical remote sensing images. The detection of ship targets often requires the target to be complete, and the overall features of the ship are used for detection and recognition. When the ship target is obscured by clouds, or the contrast between the ship target and the sea-clutter background is low, there may be incomplete targets, which reduce the effectiveness of recognition. Here, we propose a new method combining constant false alarm rate (CFAR) and superpixel segmentation with feature points (SFCFAR) to solve the above problems. Our newly developed SFCFAR utilizes superpixel segmentation to divide large scenes into many small regions which include target regions and background regions. In remote sensing images, the target occupies a small proportion of pixels in the entire image. In our method, we use superpixel segmentation to divide remote sensing images into meaningful blocks. The target regions are identified using the characteristics of clusters of ship texture features and the texture differences between the target and background regions. This step not only detects the ship target quickly, but also detects ships with low contrast and under cloud cover. In optical remote sensing, ships at sea under thin clouds are not common in practice, and the sample size generated is relatively small, so this problem is not applicable to deep learning algorithms for training, while the SFCFAR algorithm does not require data training to complete the detection task. Experiments show that the proposed SFCFAR algorithm enhances the detection of obscured ship targets under clouds and low-contrast targets in thin fog, compared with traditional target detection methods and as deep learning algorithms, further complementing existing ship detection methods.

• The model of pipeline multi-system with reconfiguration containing the decaying process and the repairing process is established, which is a multi-state reliability modeling considering the failure interaction. • Based on the Markov model of the deterioration process, the frame of the mathematical relationship between the probability of each state of the pipe unit and the decay path is established. • Propose a novel method to assess the reliability of multi-state reconfiguration pipeline system with failure interaction based on cloud inference. Traditional reliability theory cannot objectively describe the multi-state of the pipeline system with reliability analysis. In addition, studies on reliability modeling of the multi-state pipeline system assume that the states of each pipeline unit are independent. In this work, an efficient method was proposed to identify the performance relationship. Specifically, a model of a multi-state reconstruction system containing the decaying process and the repairing process was established. Also, a multi-state reliability modeling method was developed based on the performance degradation process of the pipeline system considering the failure interaction. Further, the Markov model of the degradation process was built to determine the relationship between the occurrence probability of each state and the decay path. The cloud generalization function was established using cloud inference combined with the general generation function. The pipeline system can be dynamically divided by the analytic hierarchy process according to different factors affecting the degradation of pipeline performance. A numerical example was used to demonstrate the efficiency and applicability of the proposed method by comparing it with existing methods. The proposed approaches are found to be sufficient to improve risk decision and reliability analysis of the pipeline system, which better supports the reliability-informed integrity management for the whole system. [ABSTRACT FROM AUTHOR]

Accurate estimation of the atmospheric boundary layer height (ABLH) is critically important and it mainly relies on the detection of the vertical profiles of atmosphere variables (temperature, humidity,’ and horizontal wind speed) or aerosols. Aerosol Lidar is a powerful remote sensing instrument frequently used to retrieve ABLH through the detection of the vertical distribution of aerosol concentration. A challenge is that cloud, residual layer (RL), and local signal structure seriously interfere with the lidar measurement of ABLH. A new objective technique presenting as giving a top limiter altitude is introduced to reduce the interference of RL and cloud layer on ABLH determination. Cloud layers are identified by looking for the rapid increase and sharp attenuation of the signal combined with the relative increase in the signal. The cloud layers whether they overlay the ABL are classified or are decoupled from the ABL are classified by analyzing the continuity of the signal below the cloud base. For cloud layer capping of the ABL, the limiter is determined to be the altitude where a positive signal gradient first occurs above the cloud upper edge. For a cloud that is decoupled from the ABL, the cloud base is considered to be the altitude limiter. For RL in the morning, the altitude limiter is the greatest positive gradient altitude below the RL top. The ABLH will be determined below the top limiter altitude using Haar wavelet (HM) and the curve fitting method (CFM). Besides, the interference of local signal noise is eliminated through consideration of the temporal continuity. While comparing the lidar-determined ABLH by HM (or CFM) and nearby radiosonde measurements of the ABLH, a reasonable concordance is found with a correlation coefficient of 0.94 (or 0.96) and 0.79 (or 0.74), presenting a mean of the relative absolute differences with respect to radiosonde measurements of 10.5% (or 12.3%) and 22.3% (or 17.2%) for cloud-free and cloudy situations, respectively. The diurnal variations in the ABLH determined from HM and CFM on four selected cases show good agreement with a mean correlation coefficient higher than 0.99 and a mean absolute bias of 0.22 km. Also, the determined diurnal ABLH are consistent with surface turbulent kinetic energy (TKE) combined with the time-height distribution of the equivalent potential temperature.

Accurate estimation of the atmospheric boundary layer height (ABLH) is critically important and it mainly relies on the detection of the vertical profiles of atmosphere variables (temperature, humidity,' and horizontal wind speed) or aerosols. Aerosol Lidar is a powerful remote sensing instrument frequently used to retrieve ABLH through the detection of the vertical distribution of aerosol concentration. A challenge is that cloud, residual layer (RL), and local signal structure seriously interfere with the lidar measurement of ABLH. A new objective technique presenting as giving a top limiter altitude is introduced to reduce the interference of RL and cloud layer on ABLH determination. Cloud layers are identified by looking for the rapid increase and sharp attenuation of the signal combined with the relative increase in the signal. The cloud layers whether they overlay the ABL are classified or are decoupled from the ABL are classified by analyzing the continuity of the signal below the cloud base. For cloud layer capping of the ABL, the limiter is determined to be the altitude where a positive signal gradient first occurs above the cloud upper edge. For a cloud that is decoupled from the ABL, the cloud base is considered to be the altitude limiter. For RL in the morning, the altitude limiter is the greatest positive gradient altitude below the RL top. The ABLH will be determined below the top limiter altitude using Haar wavelet (HM) and the curve fitting method (CFM). Besides, the interference of local signal noise is eliminated through consideration of the temporal continuity. While comparing the lidar-determined ABLH by HM (or CFM) and nearby radiosonde measurements of the ABLH, a reasonable concordance is found with a correlation coefficient of 0.94 (or 0.96) and 0.79 (or 0.74), presenting a mean of the relative absolute differences with respect to radiosonde measurements of 10.5% (or 12.3%) and 22.3% (or 17.2%) for cloud-free and cloudy situations, respectively. The diurnal variations in the ABLH determined from HM and CFM on four selected cases show good agreement with a mean correlation coefficient higher than 0.99 and a mean absolute bias of 0.22 km. Also, the determined diurnal ABLH are consistent with surface turbulent kinetic energy (TKE) combined with the time-height distribution of the equivalent potential temperature. [ABSTRACT FROM AUTHOR]