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Ship detection under cloudy and foggy conditions is a significant challenge in remote sensing satellite applications, as cloud cover often reduces contrast between targets and backgrounds. Additionally, ships are small and affected by noise, making them difficult to detect. This paper proposes a Cloud Removal and Target Detection (CRTD) network to detect small ships in images with thin cloud cover. The process begins with a Thin Cloud Removal (TCR) module for image preprocessing. The preprocessed data are then fed into a Small Target Detection (STD) module. To improve target–background contrast, we introduce a Target Enhancement module. The TCR and STD modules are integrated through a dual-stage supervision network, which hierarchically processes the detection task to enhance data quality, minimizing the impact of thin clouds. Experiments on the GaoFen-4 satellite dataset show that the proposed method outperforms existing detectors, achieving an average precision (AP) of 88.9%.

Laser shock ablation is incorporated into laser shock imprinting for the fabrication of papillary composite microstructures on aluminum surfaces. The primary papillary structures are fabricated using laser shock imprinting. Subsequently, secondary structures were fabricated on the surface of these primary structures using laser shock ablation, forming composite papillary microstructures. The influence of various laser shock ablation process parameters on the formation effect of these papillary composite microstructure surfaces was investigated. The results indicate that both laser shock energy and shock frequency affect the integrity of the secondary microstructure coverage on the material surface, the height of the composite microstructure, and the surface morphology. Through comparative optimization, the optimal process parameters were determined to be 675 mJ of energy and one shock ablation. Additionally, the differences in the flow behavior of metallic materials between the center and the periphery of the beam spot, caused by the shock wave, were analyzed. The wettability of the composite microstructure aluminum surface was also explored. The variation mechanism of wettability was explained by detecting changes in the contact angle on the aluminum surface at different time intervals and analyzing changes in surface chemical composition before and after aging. Specifically, after laser shock ablation, the aluminum surface contains a large number of polar groups, making it hydrophilic. During aging treatment, these polar groups continuously adsorb non-polar alkyl organic compounds, eventually leading to hydrophobicity, with a stabilized average surface contact angle of 143°. Fluorination treatment can further achieve superhydrophobicity, with a contact angle of 151° achieved shortly after processing the composite microstructure aluminum surface.

Communication transmission in the near-Moon space is a critical enabler of scientific exploration in this region. However, the communication network in near-Moon space shows trends of diversification, heterogeneity, and collaboration, posing significant challenges to the management of an integrated communication network. This paper proposes a networking routing method for near-Moon-space cross-domain network transmission. Considering the constraints of heterogeneous networks including Moon–Earth, Moon–surface, and relay transmission, the method enhances transmission routing efficiency at the network layer of near-Moon-space systems, thereby improving the overall efficiency of heterogeneous network interactions. This research focuses on the networking routing of cross-domain networks. To simplify the research problem, a mixed link resource and scheduling model of heterogeneous networks is proposed. Based on this model, a time-varying and fixed topology network sub-network clustering method was designed to reduce the complexity of the routing algorithm. A routing scheduling algorithm is provided in combination with hierarchical routing search, and related experiments and comparisons were carried out. Finally, considering the practical issues of communication relay channels and rate limitations in relay satellites, time windows and communication rate constraints were used to enhance the reliability of the simulation validation. Simulation results show that this method effectively addresses the issue of low transmission interaction efficiency in heterogeneous networks within cislunar space. Compared with previous designs, it improves link load rate by 31%, reduces average service delay by 8%, and significantly enhances link stability and load rate.

Loquat is an evergreen fruit crop which blooms from autumn–winter, and supports human beings with juicy fruit from late spring to early summer. However, the most traditional cultivars of this crop produce small fruit and bear a much lower yield than its relatives like apple, pear and peach. Large-size cultivars have long been a cherished aim of breeders for improving the production yield of loquat. Agronomic practices like panicle thinning, fruit thinning, growth regulator application, fertilization and so on are easier and more accessible ways for growers to produce large-size loquat fruit on existing production trees. Here, we develop a novel pruning method with an annual double back-cut, which provides vigorous shoot with more leaves and thicker branches for bearing much larger loquat fruit. Cellular observation determined that the vigorous shoot training method motivated cell division to produce larger loquat fruit, and that most of these cell layers were proliferated before the appearance of flower blossoms. Gene expression data of four development stages showed that EjFWL1 and EjFWL2 were notably downregulated in flower buds of the vigorously pruned tree. The data here further confirmed that the cell division capacity during flower development greatly influenced both the flower and fruit size of loquat. More importantly, we developed a novel pruning method to inhibit cell division repressors, promote cell proliferation and enlarge fruit size in loquat.

Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness.

It has been observed that the existing convolutional neural network (CNN)-based ship detection models often result in high false detection rate in low-resolution optical satellite images. This problem arises from the following factors: 1) the current 8-b rescaling schemes make the images lose some important information about ships in low-resolution imagery; 2) the effective features of ships at low resolution are far fewer than those of ships at high resolution; and 3) the detection of low-resolution ships is more sensitive to object-background contrast variation. To solve these problems, a low-resolution marine object (LMO) detection YOLO model, called LMO-YOLO, is proposed in this article. First, a multiple linear rescaling scheme is developed to quantize the original satellite images into 8-b images; second, dilated convolutions are included in a YOLO network to extract object features and object-background features; finally, an adaptive weighting scheme is designed to balance the loss between easy-to-detect ships and hard-to-detect ships. The proposed method was validated by level 1 product images captured by the wide-field-of-view sensor on the GaoFen-1 satellite. The experimental results demonstrated that our method accurately detected ships from low-resolution images and outperformed state-of-the-art methods.

Super-resolution enhances the spatial resolution of remote sensing images, yielding clearer data for diverse satellite applications. However, existing methods often lose true detail and produce pseudo-detail in reconstructed images due to an insufficient number of ground truth images for supervision. To address this issue, a prediction-to-prediction super-resolution (P2P-SR) network under a multi-level supervision paradigm was proposed. First, a multi-level supervision network structure was proposed to increase the number of supervisions by introducing more ground truth images, which made the network always predict the next level based on the super-resolution reconstruction results of the previous level. Second, a super-resolution component combining a convolutional neural network and Transformer was designed with a flexible super-resolution scale factor to facilitate the construction of multi-level supervision networks. Finally, a method of dividing the super-resolution overall scale factor was proposed, enabling an investigation into the impact of diverse numbers of components and different scale factors of components on the performance of the multi-level supervision network. Additionally, a new remote sensing dataset containing worldwide scenes was also constructed for the super-resolution task in this paper. The experiment results on three datasets demonstrated that our P2P-SR network outperformed the state-of-the-art (SOTA) methods.

In this work, we propose a high-throughput online identification method of bioaerosols based on multi-angle polarization index system (MAPIS). In the study, four categories and 10 subclasses of aerosol samples from biological and non-biological sources are detected under three incident polarization mode. Then their measured MAPIS shows that bioaerosols like pollen can be easily distinguished from other types of aerosols. Not only that, experimental results also indicate the feasibility of fine identification between different kinds of bioaerosols based on MAPIS in P and R modes. To further extract simple and optimized polarization characterization parameters suitable for bioaerosols, we analyze the multidimensional data of MAPIS by PCA then validate the aerosol recognition accuracy using the first two principal components by multiple groups of randomly mixed aerosol datasets. The comparison with PCA components based on only scattering intensity demonstrate that MAPIS can be not only applied in the specific identification of bioaerosols but also suitable for the distinction between different kinds of bioaerosols.

The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset.

The complexity of changeable marine backgrounds makes ship detection from satellite remote sensing images a challenging task. The ubiquitous interference of cloud and fog led to missed detection and false-alarms when using imagery-based optical satellite remote sensing. An off-shore ship detection method with scene classification and a saliency-tuned YOLONet is proposed to solve this problem. First, the image blocks are classified into four categories by a density peak clustering algorithm (DPC) according to their grayscale histograms, i.e., cloudless areas, thin cloud areas, scattered clouds areas, and thick cloud areas. Secondly, since the ships can be regarded as salient objects in a marine background, the spectral residue saliency detection method is used to extract prominent targets from different image blocks. Finally, the saliency tuned YOLOv4 network is designed to quickly and accurately detect ships from different marine backgrounds. We validated the proposed method using more than 2000 optical remote sensing images from the GF-1 satellite. The experimental results demonstrated that the proposed method obtained a better detection performance than other state-of-the-art methods.

Due to the fact that WorldView-2 (WV2) has a small time lag while acquiring images from panchromatic (PAN) and two multispectral (MS1 and MS2) sensors, a moving vehicle is located at different positions in three image bands. Consequently, such displacement can be utilized to identify moving vehicles, and vehicle information, such as speed and direction can be estimated. In this paper, we focus on moving vehicle detection according to the displacement information and present a novel processing chain. The vehicle locations are extracted by an improved morphological detector based on the vehicle’s shape properties. To make better use of the time lag between MS1 and MS2, a band selection process is performed by both visual inspection and quantitative analysis. Moreover, three spectral-neighbor band pairs, which have a major contribution to vehicle identification, are selected. In addition, we improve the spatial and spectral analysis method by incorporating local ERGAS index analysis (ERGAS-SNS) to identify moving vehicles. The experimental results on WV2 images showed that the correctness, completeness and quality rates of the proposed method were about 94%, 91% and 86%, respectively. Thus, the proposed method has good performance for moving vehicle detection and information extraction.

Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.

Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop’s growth and implement effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea–land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result.

Aircraft automatic detection from very high-resolution (VHR) images plays an important role in a wide variety of applications. This paper proposes a novel detector for aircraft detection from very high-resolution (VHR) remote sensing images. To accurately distinguish aircrafts from background, a circle-frequency filter (CF-filter) is used to extract the candidate locations of aircrafts from a large size image. A multi-level feature model is then employed to represent both local appearance and spatial layout of aircrafts by means of Robust Hue Descriptor and Histogram of Oriented Gradients. The experimental results demonstrate the superior performance of the proposed method.