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Abstract: Study of diluted magnetic semiconductor nanowires is one of the important topics in materials science. By using Mn–Cu alloy as the starting material, Mn doped CuO nanowire arrays have been synthesized in air at the temperature of 550 °C. X-ray diffraction measurements and scanning electron microscopic study shows that the nanowires were grown on Cu2O substrate. Transmission electron microscopic study shows the single crystal property of the nanowires. Magnetic measurements show ferromagnetic property in the Mn doped CuO nanowires with the critical temperature higher than 80 K. [ABSTRACT FROM AUTHOR]

Accurately estimating the collapse height of buildings during earthquakes holds great significance. Acquiring precise height information of buildings using dual-phase high-resolution stereo satellite images (HRSSIs) requires high-precision ground control points, which are difficult to obtain in earthquake-stricken areas. Spaceborne laser data has been validated to significantly improve the vertical accuracy of HRSSIs. Therefore, this paper first proposes the application of a combined stereo adjustment model in building collapse estimation and thoroughly verifies its feasibility. Firstly, the laser altimetry points (LAPs) acquired by GF-7 and ICESat-1 serve as elevation control points (ECPs), and the tie points from HRSSIs were used as constraint to conduct combined adjustment to improve the consistency of stereoscopic positioning accuracy between HRSSIs before and after the earthquake. Subsequently, a footprints-to-conners, coarse-to-fine, automatic building corners extraction method was performed. Avoiding the time-consuming generation of large-scale DSM while also improving the accuracy of building collapse assessment. Finally, according to the height change of building corners calculated from HRSSIs, building damage assessment was completed. According to the above methods, independent experiments were carried out respectively for the Oaxaca earthquake in Mexico in 2020 and the Kahramanmaras earthquake in Türkiye in 2023. GF-7 HRSSIs was used to verify the accuracy and stability of the proposed method for 3D assessment of building damage. The results indicate that the RMSE of elevation positioning accuracy has been greatly reduced in both regions, ranging from 10.29 m to 0.95 m and from 6.67 m to 0.99 m, respectively. This accuracy guarantees the feasibility of detecting the building floor-level collapse. And 2541 building corner points were extracted from two disaster areas, with correct corner points accounting for 85.18%. A total of 520 buildings were correctly extracted corner points, with an accuracy rate of 94.12% for assessing the degree of damage. [ABSTRACT FROM AUTHOR]

In the coastal areas of China, the eutrophication of seawater leads to the continuous occurrence of red tide, which has caused great damage to Marine fisheries and aquatic resources. Therefore, the detection and prediction of red tide have important research significance. The rapid development of optical remote sensing technology and deep-learning technology provides technical means for realizing large-scale and high-precision red tide detection. However, the difficulty of the accurate detection of red tide edges with complex boundaries limits the further improvement of red tide detection accuracy. In view of the above problems, this paper takes GOCI data in the East China Sea as an example and proposes an improved U-Net red tide detection method. In the improved U-Net method, NDVI was introduced to enhance the characteristic information of the red tide to improve the separability between the red tide and seawater. At the same time, the ECA channel attention mechanism was introduced to give different weights according to the influence of different bands on red tide detection, and the spectral characteristics of different channels were fully mined to further extract red tide characteristics. A shallow feature extraction module based on Atrous Spatial Pyramid Convolution (ASPC) was designed to improve the U-Net model. The red tide feature information in a multi-scale context was fused under multiple sampling rates to enhance the model's ability to extract features at different scales. The problem of limited accuracy improvement in red tide edge detection with complex boundaries is solved via the fusion of deep and shallow features and multi-scale spatial features. Compared with other methods, the method proposed in this paper achieves better results and can detect red tide edges with complex boundaries, and the accuracy, precision, recall, and F1-score are 95.90%, 97.15%, 91.53%, and 0.94, respectively. In addition, the red tide detection experiments in other regions with relatively concentrated distribution also prove that the method has good applicability. [ABSTRACT FROM AUTHOR]

The accurate and timely identification of the degree of building damage is critical for disaster emergency response and loss assessment. Although many methods have been proposed, most of them divide damaged buildings into two categories—intact and damaged—which is insufficient to meet practical needs. To address this issue, we present a novel convolutional neural network—namely, the earthquake building damage classification net (EBDC-Net)—for assessment of building damage based on post-disaster aerial images. The proposed network comprises two components: a feature extraction encoder module, and a damage classification module. The feature extraction encoder module is employed to extract semantic information on building damage and enhance the ability to distinguish between different damage levels, while the classification module improves accuracy by combining global and contextual features. The performance of EBDC-Net was evaluated using a public dataset, and a large-scale damage assessment was performed using a dataset of post-earthquake unmanned aerial vehicle (UAV) images. The results of the experiments indicate that this approach can accurately classify buildings with different damage levels. The overall classification accuracy was 94.44%, 85.53%, and 77.49% when the damage to the buildings was divided into two, three, and four categories, respectively. [ABSTRACT FROM AUTHOR]

Global navigation satellite system reflectometry technology (GNSS-R) is rarely used for river flow velocity inversion, and in particular, there is currently no research using the BeiDou Navigation Satellite System reflectometry technology (BDS-R) for river flow velocity inversion. In this paper, a carrier phase observation of river flow velocity inversion model is proposed. The interference phase is the integral of the Doppler frequency. The raw intermediate frequency (IF) data sets are processed through an open-loop method to obtain the Doppler frequency observation generated by river flow and then realize velocity inversion. The shore-based river current measurement was conducted on the south bank of Dashengguan Yangtze River in Nanjing city, Jiangsu Province, for nearly two hours on 22 April 2021. After realizing the inversion of river flow velocity in GPS L1, the combined inversion of BDS B1I GEO satellite and IGSO satellite is realized for the first time, which demonstrates the feasibility of river flow velocity inversion using BDS reflected signals. Compared with the real river flow velocity, the GPS L1 PRN 4 (1st period) inversion precision reaches up to 0.028 m/s (mean absolute error, MAE) and 0.036 m/s (root mean square error, RMSE). In parallel, BDS GEO 2 inversion precision can reach 0.048 m/s (MAE) and 0.063 m/s (RMSE), and BDS IGSO 10 inversion precision is 0.061 m/s (MAE) and 0.073 m/s (RMSE). These results illustrate that satellite elevation change rate and distance between specular points and current meter may have a negative effect on the accuracy of river flow velocity inversion. Specular points obstructed by obstacles or too far from the velocity meter may introduce uncertain error in both MAE and RMSE. Neither the satellite elevation nor the signal strength has an obvious correlation with inversion precision, which is consistent with the theoretical principle. [ABSTRACT FROM AUTHOR]

Due to rapid global warming, the relationship between the mass loss of the Antarctic ice sheet and rising sea levels are attracting widespread attention. The Lambert–Amery glacial system is the largest drainage system in East Antarctica, and its mass balance has an important influence on the stability of the Antarctic ice sheet. In this paper, the recent ice flux in the Lambert Glacier of the Lambert–Amery system was systematically analyzed based on recently updated remote sensing data. According to Landsat-8 ice velocity data from 2018 to April 2019 and the updated Bedmachine v2 ice thickness dataset in 2021, the contribution of ice flux approximately 140 km downstream from Dome A in the Lambert Glacier area to downstream from the glacier is 8.5 ± 1.9   Gt · a − 1 , and the ice flux in the middle of the convergence region is 18.9 ± 2.9   Gt · a − 1 . The ice mass input into the Amery ice shelf through the grounding line of the whole glacier is 19.9 ± 1.3   Gt · a − 1 . The ice flux output from the mainstream area of the grounding line is 19.3 ± 1.0   Gt · a − 1 . Using the annual SMB data of the regional atmospheric climate model (RACMO v2.3) as the quality input, the mass balance of the upper, middle, and lower reaches of the Lambert Glacier was analyzed. The results show that recent positive accumulation appears in the middle region of the glacier (about 74–78°S, 67–85°E) and the net accumulation of the whole glacier is 2.4 ± 3.5   Gt · a − 1 . Although the mass balance of the Lambert Glacier continues to show a positive accumulation, and the positive value in the region is decreasing compared with values obtained in early 2000. [ABSTRACT FROM AUTHOR]

In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy. [ABSTRACT FROM AUTHOR]

Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents' lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remote sensing image classification provides a new idea for sea ice classification. However, the level of deep learning is limited due to the influence of input size in sea ice image classification, and the deep features in the image cannot be fully mined, which affects the further improvement of sea ice classification accuracy. Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. The FPN, PAN, and SPP modules increase the mining between layer and layer features and merge the features between different layers to further improve the accuracy of sea ice classification. In order to verify the effectiveness of the method in this paper, sea ice classification experiments were performed on the hyperspectral image of Bohai Bay in 2008 and the multispectral image of Bohai Bay in 2020. The experimental results show that compared with the algorithm with fewer layers of deep learning network, the method proposed in this paper utilizes the idea of residual network to deepen the number of network layers and carries out multilevel feature fusion through FPN, PAN, and SPP modules, which effectively solves the problem of insufficient deep feature extraction and obtains better classification performance. [ABSTRACT FROM AUTHOR]

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time. [ABSTRACT FROM AUTHOR]

The feasibility of Antarctic sea ice detection based on shipborne global positioning system reflectometry (GPS-R) technology is shown in this paper. Because the permittivity of sea water is much higher than that of sea ice, the reflected left-handed circular polarized (LHCP) GPS signal (RL) reflection coefficient of sea water is markedly higher than that of sea ice. The polarization ratio of RL to the direct right-handed circular polarized (RHCP) GPS signal (DR) is used to distinguish between sea water and sea ice in this paper. The experiment was performed on the ship "XueLong" for approximately 9 days from December 2014 to January 2015 during the 31st Chinese National Antarctic Research Expedition (CHINARE 31). The sea ice concentration data with a 25 km × 25 km spatial resolution derived from the National Snow and Ice Data Center (NSIDC) are used for validation and some pictures of sea ice taken from "XueLong" are shown for comparison. The polarization ratios (RL/DR) are calculated, and the correlation coefficient between the polarization ratios (RL/DR) and the sea ice concentrations is −0.66. [ABSTRACT FROM AUTHOR]

Abstract: Alloyed wurtzite Zn49Cd51S nanocrystals with nearly 15at% Mn doped have been synthesized by the sol–gel auto-combustion method. At this work, the doping level of Mn in nanoscale sulfide semiconductors is substantially higher than early reports, so far as we know. The max doping level was established indirectly by X-ray diffraction (XRD) and photoluminescence (PL) spectroscopy: no impurities were found in the XRD pattern of the product until the doping level was increased to 15at%; a characteristic Mn 585nm emission appeared and the defect related emission diminished in the PL spectrum, when Mn was incorporated into the Zn49Cd51S nanocrystals; and the intensity of Mn 585nm emission decreased rapidly, when the doping level was increased from 5at% to 15at%. Factors that may benefit the Mn doping were discussed. [Copyright &y& Elsevier]

Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural networks (DCNNs) have performed well in semantic segmentation in recent years. However, sea-land segmentation using deep learning techniques remains a challenging task, due to the lack of a benchmark dataset and the difficulty of deciding which semantic segmentation model to use. We present a comparative framework of sea-land segmentation to Landsat-8 OLI imagery via semantic segmentation in deep learning techniques. Three issues are investigated: (1) constructing a sea-land benchmark dataset using Landsat-8 Operational Land Imager (OLI) imagery consisting of 18,000 km2 of coastline around China; (2) evaluating the feasibility and performance of sea-land segmentation by comparing the accuracy assessment, time complexity, spatial complexity and stability of state-of-the-art DCNNs methods; (3) choosing the most suitable semantic segmentation model for sea-land segmentation in accordance with Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection. Results show that the average test accuracy achieves over 99% accuracy, and the mean Intersection over Unions (mean IoU) is above 92%. These findings demonstrate that the Fully Convolutional DenseNet (FC-DenseNet) performs better than other state-of-the-art methods in sea-land segmentation, based on both AIC and BIC. Considering training time efficiency, DeeplabV3+ performs better for sea-land segmentation. The sea-land segmentation benchmark dataset is available at: . [ABSTRACT FROM AUTHOR]

Abstract To investigate the static and kinematic positioning performance with the low-cost multiple Global Navigation Satellite System (GNSS) receivers is vital for the GNSS community owing to their wide civil applications. In this contribution, we developed the real-time kinematic (RTK) algorithms for low-cost GNSS receiving module to fulfil the task of the integrated RTK positioning with US Global Positioning System (GPS) and the Chinese BeiDou Navigation Satellite System (BDS). The RTK positioning performance of a low-cost module named STA8090 and a state-of-the-art module named Novatel OEM6 was compared. In our study, the virtual reference station (VRS) data stream from QianXun SI was utilized. First, one 12-h static test was performed, achieving the centimeter-level horizontal accuracy with an RMS (Root Mean Square) of less than 1 cm. Afterwards, some kinematic experiments were conducted under four different environmental scenarios, of which positioning accuracy of approximately 20 cm was obtained. The experimental results demonstrated that the RTK positioning performance with low-cost multi-GNSS module can fulfil the current navigation capability well. [ABSTRACT FROM AUTHOR]

Oil spills, which can cause severe immediate and long-term harm to marine ecological environments for decades after the initial accident, require rapid and accurate monitoring. Currently, optical and radar satellite images are used to monitor oil spills; however, remote sensing generally needs a long revisit period. Global Navigation Satellite System reflected signals (GNSS-R) can provide all-weather and all-day ocean monitoring and is therefore more suitable for oil spill monitoring. To assess the feasibility of the BeiDou Navigation Satellite System reflected signals (BeiDou-R) in detecting oil slicks, a BeiDou-R coastal simulated experiment is performed in this study on the oil slick distribution of an oil pipeline explosion accident. We set up an observation point and selected observation satellites, and a delay-Doppler map (DDM) of an oil-slicked sea surface under coastal scenarios was created by combining the mean-square slope (MSS) model for oil-slicked/clean surfaces and the Zavorotny–Voronovich (Z–V) scattering model. DDM simulation of the coastal scenarios effectively represents the scattering coefficient distribution of the presence of an oil slick. Theoretical analysis revealed that oil slicks can be detected within a radius of less than 5 km around the specular reflection point (SP) for BeiDou-R coastal simulation. [ABSTRACT FROM AUTHOR]

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°. [ABSTRACT FROM AUTHOR]

To overcome the limitation of poor processing times for long-distance off-road path planning, an improved A-Star algorithm based on terrain data is proposed in this study. The improved A-Star algorithm for long-distance off-road path planning tasks was developed to identify a feasible path between the start and destination based on a terrain data map generated using a digital elevation model. This study optimised the algorithm in two aspects: data structure, retrieval strategy. First, a hybrid data structure of the minimum heap and 2D array greatly reduces the time complexity of the algorithm. Second, an optimised search strategy was designed that does not check whether the destination is reached in the initial stage of searching for the global optimal path, thus improving execution efficiency. To evaluate the efficiency of the proposed algorithm, three different off-road path planning tasks were examined for short-, medium-, and long-distance path planning tasks. Each group of tasks corresponded to three different off-road vehicles, and nine groups of experiments were conducted. The experimental results show that the processing efficiency of the proposed algorithm is significantly better than that of the conventional A-Star algorithm. Compared with the conventional A-Star algorithm, the path planning efficiency of the improved A-Star algorithm was accelerated by at least 4.6 times, and the maximum acceleration reached was 550 times for long-distance off-road path planning. The simulation results show that the efficiency of long-distance off-road path planning was greatly improved by using the improved algorithm. [ABSTRACT FROM AUTHOR]

Sea ice is one of the typical causes of marine disasters. Sea ice image classification is an important component of sea ice detection. Optical data contain rich spectral information, but they do not allow one to easily distinguish between ground objects with a similar spectrum and foreign objects with the same spectrum. Synthetic aperture radar (SAR) data contain rich texture information, but the data usually have a single source. The limitation of single-source data is that they do not allow for further improvements of the accuracy of remote sensing sea ice classification. In this paper, we propose a method for sea ice image classification based on deep learning and heterogeneous data fusion. Utilizing the advantages of convolutional neural networks (CNNs) in terms of depth feature extraction, we designed a deep learning network structure for SAR and optical images and achieve sea ice image classification through feature extraction and a feature-level fusion of heterogeneous data. For the SAR images, the improved spatial pyramid pooling (SPP) network was used and texture information on sea ice at different scales was extracted by depth. For the optical data, multi-level feature information on sea ice such as spatial and spectral information on different types of sea ice was extracted through a path aggregation network (PANet), which enabled low-level features to be fully utilized due to the gradual feature extraction of the convolution neural network. In order to verify the effectiveness of the method, two sets of heterogeneous sentinel satellite data were used for sea ice classification in the Hudson Bay area. The experimental results show that compared with the typical image classification methods and other heterogeneous data fusion methods, the method proposed in this paper fully integrates multi-scale and multi-level texture and spectral information from heterogeneous data and achieves a better classification effect (96.61%, 95.69%). [ABSTRACT FROM AUTHOR]

Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In addition, most of the current sea ice classification methods mainly use spectral features for shallow learning, which also limits further improvement of the sea ice classification accuracy. Therefore, this paper proposes a hyperspectral sea ice image classification method based on the spectral-spatial-joint feature with deep learning. The proposed method first extracts sea ice texture information by the gray-level co-occurrence matrix (GLCM). Then, it performs dimensionality reduction and a correlation analysis of the spectral information and spatial information of the unlabeled samples, respectively. It eliminates redundant information by extracting the spectral-spatial information of the neighboring unlabeled samples of the labeled sample and integrating the information with the spectral and texture data of the labeled sample to further enhance the quality of the labeled sample. Lastly, the three-dimensional convolutional neural network (3D-CNN) model is designed to extract the deep spectral-spatial features of sea ice. The proposed method combines relevant textural features and performs spectral-spatial feature extraction based on the 3D-CNN model by using a large amount of unlabeled sample information. In order to verify the effectiveness of the proposed method, sea ice classification experiments are carried out on two hyperspectral data sets: Baffin Bay and Bohai Bay. Compared with the CNN algorithm based on a single feature (spectral or spatial) and other CNN algorithms based on spectral-spatial features, the experimental results show that the proposed method achieves better sea ice classification (98.52% and 97.91%) with small samples. Therefore, it is more suitable for classifying hyperspectral sea ice images. [ABSTRACT FROM AUTHOR]