ABSTRACT Generally, there are two problems restrict the intracranial haemorrhage (ICH) segmentation task: scarcity of labelled data, and poor accuracy of ICH segmentation. To address these two issues, we propose a semi‐supervised ICH segmentation model and a dedicated ICH segmentation backbone network. Our approach aims at leveraging semi‐supervised modelling so as to alleviate the challenge of limited labelled data availability, while the dedicated ICH segmentation backbone network further enhances the segmentation precision. An augmented multiple perturbation dual mean teacher model is designed. Based on it, the prediction accuracy may be improved by a more stringent confidence‐weighted cross‐entropy (CW‐CE) loss, and the feature perturbation may be increased using adversarial feature perturbation for the purpose of improving the generalization ability and efficiency of consistent learning. In the ICH segmentation backbone network, we promote the segmentation accuracy by extracting both local and global features of ICH and fusing them in depth. We also fuse the features with rich details from the upper encoder during the up‐sampling process to reduce the loss of feature information. Experiments on our private dataset ICHDS, and the public dataset IN22SD demonstrate that our model outperforms current state‐of‐the‐art ICH segmentation models, achieving a maximum improvement of over 10% in Dice and exhibiting the best overall performance.

The detection and recognition of ships hold significant practical implications for both military and civilian departments. Recent advancements in deep learning technology have led to notable progress in this field. However, the precise detection and recognition of ships remains a challenge, especially in multi-source remote sensing images, due to their different feature expression and resolution. Moreover, the effectiveness of the existing models in complex environments still needs to be further validated. Therefore, in this letter, we construct a new ship dataset which contains five distinct ship categories under a number of complex environments. Different from existing datasets that are based on unimodal data, the new dataset uses multi-modal remote sensing images with different resolutions. On this basis, this study introduces an adaptable and robust ship detection and recognition model, namely SIDE-YOLO. The model incorporates an super-resolution convolutional neural network (SRCNN) and side window (SRSW) based contour feature enhancement module, a SimAM feature attention Resblock (ResBlockSA), and a DConv-based cross-scale feature enhancement block (DCFB), to strengthen the ship edge features, adaptively improve the problem of limited sample in SAR images, and amalgamate multi-scale ship context information, respectively. Validation results demonstrate that the proposed model achieves an average precision (AP) rate [mean AP (mAP)] of 84.31%, surpassing state-of-the-art ship recognition models. Notably, for the civilian ship category, the model exhibits 2.17% and 2.63% higher recall and AP in complex scenes, respectively.

To improve the performance of object recognition under artificial prosthetic vision, this study proposes a two-stage method. The first stage is to extract the saliency and edge Mask of the object (SMP, EMP). Then, the irregular visual information of the object is processed using Irregularity Correction (IC). We design eye-hand coordination tasks and simulate artificial vision with retinal prostheses to validate strategy effectiveness, and select direct pixelation (DP) as a control group. Each subject retained a phosphene map in the same stochastic pattern in all his/her trails. The real-time experimental results showed that the deep saliency-based optimization strategies improved the performance of the subjects when completing tasks, in terms of head movement, recognition accuracy, and response time, and counts for successful small-objects recognition. The subjects have the smallest-scale average head movement (76.53 deg ± 20.75 deg), higher average objects recognition accuracy (91.18% ± 2.52%), and less time for finishing the task (35.71 s ± 8.66 s) and better successful search times of the small target objects (1.35 ± 0.33) under the SMP strategy. When integrating with IC, subjects' average performances have further improved to 63.39 ± 15.38 deg, 94.22% ± 3.94%, 25.76 s ± 6.24 s and 1.05 ± 0.30 respectively, which also significantly outperformed the DP condition. These results indicated that when utilizing the deep-learning-based saliency detection and IC processing, subjects could shorten the searching process and were able to discern the target objects more reliably. This work could be informative to future prosthetic devices considering implementation with the technique of artificial intelligence.

GNSS-R technology for the retrieval of sea surface wind speed (SW) has gradually matured, and many research results in terms of methodology and accuracy have been obtained. Multisource data fusion has been a major trend in remote sensing research in recent years. However, there are few fusion algorithms in SW retrieval, and most of them retrieve the SW of a single data source. Based on the principle of CYGNSS forward scattering and HY-2B microwave scatterometer (HSCAT-B) backscattering, this paper proposes a Fusion Model (FM) of SW based on CYGNSS and HSCAT-B. For CYGNSS SW inversion using the FM, there is no need to input HSCAT-B data, and the accuracy of CYGNSS SW inversion above 10 m/s is improved. Based on the true SW data of the European Center for Medium-Range Weather Forecasts (ECMWF), the root mean square error (RMSE) of SW inversion is improved from 2.517 m/s and 1.645 m/s with a single data source to 1.527 m/s with the FM. To further correct the outliers of the FM, the result fitting model is added after the FM. The experimental results show that the RMSE of the result fitting model is improved from 1.527 m/s for the FM to 1.489 m/s. Finally, CYGNSS L2 SW and the National Data Buoy Center (NDBC) data is used to verify the inversion results, the RMSE of the result fitting model is 1.688 m/s and 1.60 m/s, respectively. The results prove the feasibility of a fusion algorithm for SW using multisource data.

Highway crack segmentation is a critical task for highway infrastructure monitoring and maintenance. While imagery from unmanned aerial vehicles (UAVs) is applied to the task of highway crack segmentation, it has great prospects in terms of speed and range. However, it is difficult to accurately identify road cracks from UAV remote sensing images, because the cracks are very narrow and small, often containing only a few pixels. To improve the segmentation of road cracks in UAV images, this study proposed an improved identification technique based on the U-Net architecture enhanced with a convolutional block attention module, an improved encoder, and the strategy of fusing long and short skip connections. A public road crack dataset was relabelled for network training and a UAV remote sensing road crack dataset containing 1157 images was used to verify the generalization ability of the enhanced network model. Results showed that the proposed method could effectively predict highway cracks in UAV images, with mean intersection over union (mIoU) of 77.47% and crack accuracy of 68.38%, which was better than the traditional U-Net model and some traditional semantic segmentation models. The proposed network is trained quickly by public dataset and can predict the road cracks on the new UAV images with high crack accuracy. This study provides an effective solution for the need to quickly grasp the damage status of roads over a wide area in the case of earthquake and other natural disasters. The highway crack segmentation benchmark dataset has been open sourced at:https://github.com/zhhongsh/UAV-Benchmark-Dataset-for-Highway-Crack-Segmentation.

Using Cyclone Global Navigation Satellite System (CYGNSS) L1 data with large amount and wide coverage, this paper establishes a sea surface wind direction retrieval model based on three machine learning algorithms. Wind direction will cause the asymmetry of Delay Doppler Map (DDM). Based on this, this paper extracts two angle characteristic parameters from DDM. Compared with CYGNSS full DDM, L1 compact DDM has a reduced dimension. Therefore, this paper expands more characteristic parameters, including L1 parameters and geophysical parameters such as wind speed, mean sea surface pressure (MSL), sea surface temperature (SST). Wind speed, direction, MSL and SST are from the European Centre for Medium-Range Weather Forecasts (ECMWF). After data preprocessing, the experimental data set is generated. Based on this data set, this paper establishes SVM, BP and CNN wind direction retrieval models and verifies their model performance and generalization performance. In addition, a filter that can optimize the accuracy of CNN model is constructed, and the retrieval effect under different wind direction intervals is further studied. The results show that the accuracy of CNN model for L1 data is higher than that of SVM and BP, and the retrieval error of global sea surface wind direction after filtering is less than 20°. The accuracy difference of different wind direction intervals also has a significant impact on the wind direction retrieval results.

This letter is dedicated to evaluating the potential use of reflected signals from the BeiDou Navigation Satellite System for retrieving the thickness of sea ice. Accurate phase altimetry results over sea ice would be helpful for detecting the sea ice thickness. Here, a new phase processing approach is proposed to estimate the adaptable phase altimetry result. During the data processing, fake wrap points in the residual interferometric phase are replaced by fit values; moreover, the phase altimetry result is shown to reach centimeter accuracy. A coastal experiment was performed from January 22 to February 19, 2016, in Dashentang, Tianjin, China. The sea ice thicknesses calculated from the phase altimetry results and Archimedes' principle range from 5 to 25 cm, which are consistent with the thicknesses of samples collected during the experimental period in Bohai Bay near Dashentang.

With the development of the Chinese BeiDou satellite navigation system, the applications of BeiDou reflected (BeiDou-R) signals would play a key role in Global Navigation Satellite System reflected signals. Different from other navigation systems, the BeiDou satellite navigation system has certain unique characteristics, and the Geostationary Earth Orbit (GEO) satellite is one of them. The aim of this letter is to prove the feasibility of coastal ocean phase altimetry using BeiDou GEO reflected signals. The coastal experiment was performed from October 18, 2014, to October 19, 2014, in Dayang Shan, Zhejiang, China. This is the first coastal ocean phase altimetry experiment using BeiDou GEO reflected signals. The phase altimetry results can invert the slope of ocean surface height variation (in time), and its highest accuracy can reach centimeter level.

The paper explored a method to obtain accurate lake surface heights using Global Navigation Satellite System (GNSS) Coarse/Acquisition Code (C/A)reflected from the ocean surface. The method is referred to as Global Navigation Satellite System-Reflection (GNSS-R) (C/A) code altimetry. It focuses on the extraction of the delay between the direct and reflected signal and inverting ocean altimetry with the delay and the geometric relationship. The ocean altimetry results are consistent with the height results of the differential positioning analysis of the data collected by a handheld GIS. The results show that we can achieve meter level height in seven minutes average.

In order to realize the optimization selection and utilization of manufacturing resource in the development process of complicated product, a kind of multipurpose optimization decision-making method based on stratification was brought forth. Facing towards hierarchy and decomposition of task in the development process, this paper combined the restrictions and evaluation criterions of resource selection with product development task, and guaranteed the practicability of resource selection. By improved method of multipurpose optimization decision-making, combining with two utility evaluation functions under the standard of gain target and profit loss target, the principle and process of resource optimization selection were depicted in detail which based on the targets such as response time, service quality, cost and security, etc, and the sort and selection of resource dispatch were realized. Finally, the feasibility of algorithm was verified by a real instance in the engineering field.

In order to realize the target to analyze and control microcosmic process of product development according to different level and region in different phase, a kind of planning and design method of product development process was brought forth which based on self-organizing and organized methodology. In the process of task stratification and decomposition and product development based on restraint, the restraints and influence factors in connection with process were reflected and embodied by logic state and evaluation probability among activity nodes. By applying nature and principle of reachable matrix in graphics theory, activity sequence and its organization route of product development were analyzed quantitatively, macroscopic process management and microcosmic process control of product development were realized, and the organization mode, and make state evolution and work flow of development activity be observed and analyzed under the transparent and controllable environment all the time. All these help project manager to explicit the primary route and key point juncture of product development process, to overcome the difficulty of traditional method in the way of modeling and analysis by synthesizing multiple elementals and crossing entire process.