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In complex maritime scenarios where the grayscale polarity of ships is unknown, existing infrared ship detection methods may struggle to accurately detect ships among significant interference. To address this issue, this paper first proposes an infrared image smoothing method composed of Grayscale Morphological Reconstruction (GMR) and a Relative Total Variation (RTV). Additionally, a detection method considering the grayscale uniformity of ships and integrating shape and spatiotemporal features is established for detecting bright and dark ships in complex maritime scenarios. Initially, the input infrared images undergo opening (closing)-based GMR to preserve dark (bright) blobs with the opposite suppressed, followed by smoothing the image with the relative total variation model to reduce clutter and enhance the contrast of the ship. Subsequently, Maximally Stable Extremal Regions (MSER) are extracted from the smoothed image as candidate targets, and the results from the bright and dark channels are merged. Shape features are then utilized to eliminate clutter interference, yielding single-frame detection results. Finally, leveraging the stability of ships and the fluctuation of clutter, true targets are preserved through a multi-frame matching strategy. Experimental results demonstrate that the proposed method outperforms ITDBE, MRMF, and TFMSER in seven image sequences, achieving accurate and effective detection of both bright and dark polarity ship targets. [ABSTRACT FROM AUTHOR]

Cultural heritages are invaluable and non-renewable resources. Existing warning mechanisms usually rely on degradation analysis to assess environmental risk factors. However, they have limitations such as complex research, poor generalization, and inadequate warnings. To address these issues, we propose a hybrid model that combines the long short-term memory network (LSTM) and attention mechanisms with environmental factors to detect anomalies in cultural heritage. The attention mechanism extracts temporal dependencies, while LSTM captures global long-term patterns, enhancing the reconstruction capability. Additionally, we introduce the seasonal, trend, shapelet, and mixed anomalies to enrich the positive and negative samples and propose a novel threshold extraction method to reduce the reliance on prior knowledge. Extensive experiments demonstrate that LSTM-Attention outperforms previous methods, with a root mean square error (RMSE) of 34.328, mean absolute error (MAE) of 27.060, and the largest area under the receiver operating characteristic curve (AUC) value, highlighting the superiority and effectiveness of our method. The ablation study proves the contribution of the attention mechanism, and the reconstruction step can be flexibly adjusted according to practical needs. Finally, the algorithm has been successfully implemented in engineering and employed in practice, providing valuable guidelines for the preservation of ancient buildings worldwide. [ABSTRACT FROM AUTHOR]

In this paper, based on the idea of "step-by-step accuracy", a novel multiscale feature-based infrared ship-detection method (MSFISD) is proposed. The proposed method can achieve efficient and effective infrared ship detection in complex scenarios, which may provide assistance in applications such as night surveillance. First, candidate regions (CRs) are extracted from the whole image by extracting the sea–sky line and region of interest (ROI). The real sea–sky line is extracted based on the gradient features enhanced by large-scale gradient operators. The coarse segmentation results are obtained by the optimization method and are then refined by incorporating the edge features of the ship to reduce false alarms and obtain the CRs. Second, by analyzing the shape features of ships, the feature quantity is established, and the ships in CRs are finally accurately segmented. Experimental results demonstrate that compared with the other five methods, the proposed method has higher detection accuracy with a lower false-alarm rate and performs better in complex sea scenarios. [ABSTRACT FROM AUTHOR]

In this paper, principal component analysis is introduced to form a scene-based nonuniformity correction method for infrared focal plane arrays. The estimation of the gain and offset of the infrared detector and the correction of nonuniformity based on the neural network method with a novel estimation of desired target value are achieved concurrently. The current frame and several adjacent registered frames are decomposed onto a set of principal components, and then the first principal component is extracted to construct the desired target value. It is practical, forms fewer ghosting artifacts, and considerably promotes correction precision. Numerical experiments demonstrate that the proposed method presents excellent performance when dealing with clean infrared data with synthetic pattern noise as well as the real infrared video sequence. [ABSTRACT FROM AUTHOR]

In infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of the background, which seriously hinders the detection of targets. To achieve the detection of dark-polarity ship targets in such environments, a dual-channel threshold segmentation method based on local low-gray region detection and geometric features judgment is proposed in this paper. In one channel, adaptive threshold segmentation is performed on the low-gray regions of the acquired image and combined with geometric features to obtain a finer segmentation result. In the other channel, adaptive segmentation is performed on the preprocessed image, and potential backgrounds that may be finely segmented as targets are filtered out based on an area threshold. Finally, the results of the two channels are multiplied and fused to obtain an accurate segmentation result. Experimental results demonstrate that the proposed algorithm outperforms the comparison algorithm in subjective and objective evaluations. The proposed algorithm in this paper not only achieves a low false alarm rate but also exhibits a higher detection rate, and the average detection rate in the test sequence surpasses 95%. [ABSTRACT FROM AUTHOR]

Impervious surfaces play an important role in urban planning and sustainable environmental management. High-spatial-resolution (HSR) images containing pure pixels have significant potential for the detailed delineation of land surfaces. However, due to high intraclass variability and low interclass distance, the mapping and monitoring of impervious surfaces in complex town–rural areas using HSR images remains a challenge. The fully convolutional network (FCN) model, a variant of convolution neural networks (CNNs), recently achieved state-of-the-art performance in HSR image classification applications. However, due to the inherent nature of FCN processing, it is challenging for an FCN to precisely capture the detailed information of classification targets. To solve this problem, we propose an object-based deep CNN framework that integrates object-based image analysis (OBIA) with deep CNNs to accurately extract and estimate impervious surfaces. Specifically, we also adopted two widely used transfer learning technologies to expedite the training of deep CNNs. Finally, we compare our approach with conventional OBIA classification and state-of-the-art FCN-based methods, such as FCN-8s and the U-Net methods. Both of these FCN-based methods are well designed for pixel-wise classification applications and have achieved great success. Our results show that the proposed approach effectively identified impervious surfaces, with 93.9% overall accuracy. Compared with the existing methods, i.e., OBIA, FCN-8s and U-Net methods, it shows that our method achieves obviously improvement in accuracy. Our findings also suggest that the classification performance of our proposed method is related to training strategy, indicating that significantly higher accuracy can be achieved through transfer learning by fine-tuning rather than feature extraction. Our approach for the automatic extraction and mapping of impervious surfaces also lays a solid foundation for intelligent monitoring and the management of land use and land cover. [ABSTRACT FROM AUTHOR]

The power consumption and latency of existing MAC protocols for wireless sensor networks (WSNs) are high in heterogeneous convergecast, where each sensor node generates different amounts of data in one convergecast operation. To solve this problem, we present W-MAC, a workload-aware MAC protocol for heterogeneous convergecast in WSNs. A subtree-based iterative cascading scheduling mechanism and a workload-aware time slice allocation mechanism are proposed to minimize the power consumption of nodes, while offering a low data latency. In addition, an efficient schedule adjustment mechanism is provided for adapting to data traffic variation and network topology change. Analytical and simulation results show that the proposed protocol provides a significant energy saving and latency reduction in heterogeneous convergecast, and can effectively support data aggregation to further improve the performance.