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Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Geology is the property of Urban Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Building segmentation in high resolution remote sensing images is one of the hotspots in remote sensing image research. The diversity of building scales in high-resolution remote sensing images easily leads to wrong segmentation, missing segmentation and fuzzy boundaries. In order to solve the above problems, this paper proposes a remote sensing building image segmentation network based on U-Net network structure with double coder U-shaped network (DCU-Net). DCU-Net adds a parallel coding path to U-Net to form a dual-path coding structure. Dense residual coding module (DRCM) and multi-scale dilated convolutional coding module (MDCCM) are designed in the encoding stage to enhance multi-scale feature extraction. The dual hybrid attention module (DFAM) is added to the network to enhance the expression ability of the network for features. In order to verify the effectiveness of the network, experiments are carried out on WHU and Massachusetts datasets. The recall, F1 and intersection over union ratio indicators reach 91.26%, 92.33% and 86.15% on WHU dataset, and reach 81.64%, 84.33% and 82.72% on Massachusetts Buildings dataset. The results show that DCU-Net has high extraction accuracy for building extraction at different scales.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mangroves are unique coastal blue carbon ecosystems in tropical and subtropical areas. However, the dynamic changes of their carbon storage are rarely reported. Based on ground sample points and WorldView-3 high-resolution remote sensing images obtained in 2017, we identified the canopy of dominant mangrove communities in Futian mangrove utilizing random forest algorithm and object-oriented classification methods, and inverted and calculated the area of each dominant community. We then calculated the carbon storage of each dominant community combining the field survey data in 2017, 2020 and 2023, and obtained the spatial distribution and interannual dynamic changes of carbon storage of mangrove communities. The results were as follows: (1) The overall accuracy of the random forest algorithm for canopy identification was 82.29%, with a Kappa coefficient of 0.77; Futian mangrove spaned an area of 93.84 hm², with Kandelia obovata having the largest distribution area of 49.96 hm², followed by Avicennia marina, Sonneratia caseolaris, S. apetala, and Bruguiera gymnorhiza, with respective areas of 26.23, 8.90, 6.52, and 0.50 hm². (2) The total carbon storage of Kandelia obovata community was the highest, followed by Avicennia marina, Sonneratia caseolaris, S. apetala, and Bruguiera gymnorhiza the lowest. The carbon density in Sonneratia apetala and S. caseolaris community showed an increasing trend, and S. apetala community revealed the highest among the five dominant communities. The carbon density of Kandelia obovata community increased first and then decreased, while Avicennia marina community showed a downward trend consistently, and carbon density in Bruguiera gymnorhiza community did not vary significantly. In summary, the carbon storage of mangrove dominant communities in Futian did not change much from 2017 to 2023. The carbon sequestration capacity of mangrove in Kandelia obovata, Sonneratia apetala and S. caseolaris communities was stronger. The carbon density of Avicennia marina community decreased year by year, while that of Bruguiera gymnorhiza community was relatively stable. These results provide foundational data for evaluating the carbon sequestration capacities of different dominant communities in Futian mangrove, in tandem with scientific support for subsequent mangrove restoration and management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objects in remote sensing images are small in size, unevenly distributed, and highly coupled. In view of the current situation that the feature extraction information of remote sensing image super resolution models is single and underutilized, this study proposes a multipath parallel multi scale feature reuse network model to improve performance of image reconstruction. The model fuses feature information extracted from multiple network re- sidual blocks using a structure of local feature cascade and global feature fusion, where each residual block is serially connected by two multi scale convolutional units. The multiscale convolution units construct multiple branches to extract image features in parallel through cross fusion of feature information. At the same time, short skip connec- tions are introduced to strengthen feature reuse between different branches, and long skip connections are introduced to strengthen feature fusion at different depths of the network. When the amplification factor is 4, the peak signal to-noise ratios of the model on the two test sets are 29.653 1 dB and 29.037 4 dB respectively, and they are signifi- cantly improved compared with the test results of other models. Therefore, the proposed model has a good effect on super resolution reconstruction of remote sensing images. [ABSTRACT FROM AUTHOR]

Object detection in remote sensing images is a critical issue in the field of object detection. Currently, most object detection models that using deep learning add attention mechanism during the unidirectional feature fusion process, enhancing various types of objects indiscriminately and failing to highlight small objects. In order to achieve better detection results, an asymmetric high and low-level modulation mechanism is introduced, constructing feature maps that consider shallow detail information and advanced semantic information with the aim of enhancing the characteristics of small objects. Additionally, the DIoU loss function is used instead of the original SmoothL1 loss function to improve model detection accuracy and convergence speed. Furthermore, flexible context information is introduced into in the region of interest classification task to improve the accuracy of small objects classification. Experiments demonstrate that the proposed method achieves good performance on DIOR and NWPU VHR10 datasets. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of poor reconstruction due to the blurring of feature targets in remote sensing images and the influence of background noise, in this paper, a super-resolution reconstruction algorithm for remote sensing images incorporating two-branch semantic enhanced perception is proposed. Firstly, a global-local spatial attention module is designed to enhance the semantic representation of features at different scales of spatial global-local, and at the same time strengthen the discriminative ability of the network for effective feature groups. Secondly, a channel grouping-aggregation attention module is proposed to enhance the model’s discriminative ability for ground objects features by designing feature grouping-aggregation and channel attention modules, and strengthen the model’s ability to focus on effective feature channels. Experiments show that on the UC Merced dataset, the PSNR reaches 34.397 dB, 29.920 dB and 28.128 dB respectively at the ×2/×3/×4 multiplier, and the structural similarity reaches 0.931, 0.834 and 0.791 at the ×2/×3/×4 multiplier. On the AID dataset, the PSNR reaches 32.524 dB, 29.317 dB and 27.522 dB respectively at the×2/×3/×4 multiplier, and the structural similarity reaches 0.895, 0.829 and 0.721 at the ×2/×3/×4 multiplier. Compared with other mainstream algorithms, both indices are improved, and the edge and regional details of reconstructed images are better, effectively overcoming the problems of fuzzy feature information of ground objects and background noise, which lead to poor reconstruction effect of remote sensing images.

Urbanization is an important factor in promoting socio-economic development and influencing regional ecological security patterns, and the stable coordination of the ecological environment and urbanization is a guarantee of achieving sustainable regional development. In order to balance the coupling coordinated relationship between urbanization and ecological environment in the urbanization development process of urban agglomerations, the 23 counties of Changsha-Zhuzhou-Xiangtan urban agglomeration were taken as the research object, the land use, nighttime lighting and MODIS data were comprehensively collected; based on Google Earth Engine (GEE) platform, the nighttime lighting index and remote sensing ecological index were constructed to characterize the level of urbanization and ecological environment quality respectively from 2002 to 2022, and the coupling coordination degree model was used to analyze the hierarchical clustering and spatial-temporal differences of the coupling coordination between urbanization and ecological environment in Changsha-Zhuzhou-Xiangtan urban agglomeration. The results show that (1) from 2002 to 2022, the Changsha-Zhuzhou-Xiangtan urban agglomeration experiences a significant change in heat, decreasing by 29.26%; and the ecological quality of counties, except for Yanling county, decrease to different degrees, showing an inverted N-type or M-type trend, with the greatest decrease in remote sensing ecological indexes in Kaifu district, Tianxin district, Tianyuan district and other counties, where there are many urban construction activities; (2) the urbanization level of Changsha-Zhuzhou-Xiangtan urban agglomeration increases, and the nighttime lighting index is on the rise overall, but the growth rate in 2022 is lower than that in 2017, the development between counties is uneven, and the counties with higher levels concentrate in Changsha; (3) most counties in Changsha-Zhuzhou-Xiangtan urban agglomeration are in the stage of transition from antagonism to friction from 2002 to 2022, and the overall coupling degree is at a high level in 2012, but 17 counties fall back to a lower stage in 2017. In 2022, most counties in Changsha-Zhuzhou-Xiangtan urban agglomeration have reached a state of harmonization in terms of the coupling degree of coordination, and some counties are out of harmony for a long period, and the overall coupling and coordination situation are better, but the spatial distribution is unbalanced, with the characteristics of high in the north-central part and low in the south. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Investigation & Surveying is the property of Railway Investigation & Surveying Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Deep convolutional neural network (DCNN) is widely used in automatic road extraction from high-resolution remote sensing images (HRSIs). However, the existing methods make it difficult to model the context relationship of pixels in the predicted results. To solve this problem, some studies have used fully connected conditional random field (FullCRF) to perform secondary optimization of semantic segmentation results combined with context information, but the discontinuity problem of road structure cannot be effectively improved. Methods: In order to improve the integrity of road structure, this paper proposes a short range conditional random filed (SRCRF) model combined with DCNN. SRCRF mainly includes a unary potential function based on road pre-segmentation, a binary potential function based on spectral spatial features, and a K-neighborhood mean field inference algorithm. First, the prior knowledge of road pre-segmentation results is obtained by using the powerful feature extraction capability of DCNN as the unary potential function of SRCRF. Then, the dependence of the binary potential function defined by the linear combination of Gaussian kernel functions on the surrounding nodes is modeled. The binary potential function enables the classification results to have local consistency,that is, adjacent pixels with similar spectral features have the same label. Finally, the K-neighborhood mean field inference algorithm based on the mean-field approximation inference algorithm optimizes the inference range to make full use of the spatial context information and spectral feature context information of the road, and then calculates the optimal label corresponding to each pixel based on the space and spectral feature to optimize the road accurately. The convolution method is adopted to control the inference range of SRCRF within the radius of K in order to improve the proportion of feature vectors. Results: The experimental results show that SRCRF not only alleviates the transition smoothness of FullCRF, but also alleviates the structural discontinuity and incompleteness in the road acquisition results of HRSIs. In Zimbawe-Roads dataset and Cheng-Roads dataset, F1-scores of SRCRF are increased by about 4.01% and 3.73% respectively compared with DCNN, and about 3.25% and 2.28% respectively compared with FullCRF. Conclusions: The proposed SRCRF combines the advantages of DCNN, optimizes the fully connected structure of traditional conditional random fields into the K-neighborhood structure, reduces the inference scope, and improves the proportion of feature vectors. Compared with FullCRF, SRCRF can make better use of image color features and spatial features to accurately optimize the road extraction results of deep learning output. The performance of SRCRF is improved compared with DCNN and FullCRF, and the time is shortened by one order of magnitude compared with FullCRF. In the future, we will further investigate the potential for learning Gaussian features and investigate more complex architecture of conditional random field to better capture global context information. Additionally, we are particularly interested in exploring the application potential of SRCRF in other fields, such as building extraction, vehicle extraction, lake extraction, etc. [ABSTRACT FROM AUTHOR]

With the rapid development of the city, the process of urbanization is also accelerating, in order to adapt to the rapid development of economic society and the growth rate of population, followed by the increase of urban land. In order to better control the urban spatial expansion, it is particularly important to study the urban spatial expansion and driving force. In this paper, remote sensing images of five years (1981, 1991, 2001, 2011 and 2021) in the built-up area of Xuzhou city are used as data sources, and remote sensing information extraction technology is used to obtain urban expansion information of Xuzhou city. The characteristics of urban spatial expansion are analyzed from four perspectives: quantity, morphology, center of gravity and direction. The regression model is used to explore the main driving factors of Xuzhou’s urban expansion. The results show that: (1) The overall urban expansion intensity is 26% during the 40 years from 1981 to 2021, and the average annual expansion area is 36.03 km2, among which 2001-2011 is the most intense period of urban expansion intensity; (2) The fractal dimension and compactness of urban space expansion tend to be generally stable, making urban space more regular and compactness improved; (3) The overall urban spatial expansion develops in the east, southeast and south direction, and the center of gravity of the city gradually moves to the southeast direction; (4) The expansion of urban space increases significantly in the east, south and southeast, and the urban space as a whole presents a banded expansion; (5) The results of regression analysis show that population, infrastructure and policy planning factors have a significant impact on the urban spatial expansion of Xuzhou city, and economic factors will also have a certain impact on the urban development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, there are still some problems in the remote sensing target detection algorithm based on the general YOLO (you only look once) series, such as not making full use of the global context information of the image, not narrowing the semantic gap in the feature fusion pyramid part, and not suppressing the interference of redundant information. On the basis of combining the advantages of YOLO algorithms, this paper proposes GUS-YOLO (network of global context extraction unit and attention gate-based YOLOS) algorithm. It has a backbone network Global Backbone that can make full use of global context information. Other than that, this algorithm introduces the Attention Gate module into the top-down structure of the fused feature pyramid, which can emphasize the necessary feature information and suppress redundant information. Furthermore, this paper designs the best network structure for the Attention Gate module and proposes the feature fusion structure U-Net of proposed network. Finally, because the ReLU activation function may lead to the problem that the model gradient is no longer updated, the Attention Gate module uses a learnable SMU (smooth maximum unit) activation function, which can improve the robustness of the model. On the NWPU VHR-10 remote sensing dataset, this algorithm achieves 1.64 percentage points and 9.39 percentage points performance improvement on mAP0.50 and mAP0.75 respectively compared with YOLOV7. Compared with the current 7 mainstream detection algorithms, this algorithm achieves better detection performance.

Timely and accurate acquisition of winter wheat distribution is beneficial to the estimation of grain yield and optimization of planting structure. However, the previous means of acquisition cannot fully meet the needs of large-scale applications in recent years. Deep learning can be expected to introduce into agricultural remote sensing, in order to obtain the spatial distribution of agricultural resources, and further realize the development of agricultural modernization. Taking the winter wheat as the target object, the high-resolution Gaofen-2 remote sensing images were utilized to extract the winter wheat over a large area. Winter wheat was scattered distribution with the different field shapes in the remote sensing images. At the same time, the complex features were contained in the remote sensing images, leading to the interference in the recognition of winter wheat. In this study, a CAHRNet semantic segmentation network model was proposed to enhance the recognition accuracy and fuzzy details of field edges in the spatial distribution of winter wheat. The improved model first replaced the backbone feature network from ResNet with a high-resolution network HRNet. A unique parallel structure was connected to the multiple layers of sub-networks. The multiple sub-networks were continuously interacted with each other to repeatedly fuse the feature generated by multi-scale sub-networks. The spatial feature was enriched to rapidly locate the target object. The network was then maintained to extract the high-resolution feature. Secondly, the CAHRNet model was used to extract the spatial distribution of the winter wheat. The OCR module was introduced to aggregate the contextual feature of the target object. The extracted feature was classified by the backbone network. The aggregation of all the object regions was then evaluated to divide the regions into different categories. The correlation between the regions and the pixel points was enhanced to clarify the approximate location of the target object. Lastly, coordinate attention was introduced to capture the remote dependencies and retain the precise location of the target object, according to the dual spatial orientations. The correlation between multiple pixel points was enhanced to more accurately locate the exact position of the target object. The coordinate attention enhanced the spatial features of the target object and then reduced the detail loss caused by the model during feature extraction. Much more details were obtained than before in the prediction. The CAHRNet network model enhanced the supervision of the spatial features of the target object. The spatial relationship between the winter wheat and background pixels was established to enhance the region-pixel correlation. At the same time, the contextual feature of individual pixel points was utilized to enhance the pixel-point to pixel-point attention. The edge details of the target object region were preserved to highlight the overall features of winter wheat. The segmentation accuracy was improved to realize the fine extraction of winter wheat spatial distribution. The experimental results show that the optimal evaluation was achieved in each index on the high-resolution remote sensing dataset, with the MIoU and PA reaching 81.72% and 97.08%, respectively. The MIoU performance was improved by 9.90 and 2.44 percentage points, respectively, compared with the typical U-Net and DeepLabv3+, while the PA model was improved by 6.80 and 1.59 percentage points, respectively. The finding can provide technical support for the further acquisition of winter wheat crop distribution. [ABSTRACT FROM AUTHOR]

Since the objects in remote sensing images have the characteristics of arbitrary direction, dense distribution and large scale differences, object detection in remote sensing images has become a challenging problem. Aiming at this problem, this paper systematically reviewed the work related to rotating object detection in deep learning remote sensing images in recent three years. Firstly, this paper introduced the representation method and characteristics of rotated bounding box. Then, it analyzed the existing methods of rotating object detection in remote sensing images from four aspects: the feature extraction network, the generation of rotating anchor and candidate bounding box, the label allocation and sampling strategy, and the loss function. Next, it introduced the commonly used remote sensing image datasets for rotated object detection, and compared the performance of different algorithms. Finally, this paper prospected rotating object detection in remote sensing images. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bars are one of the important geomorphic features in rivers. Studying the changes and morphological characteristics of bars can provide basic information for understanding the evolution of rivers. The valley of the middle section of the Yarlung Tsangpo River exhibits an alternating pattern of wide and narrow segments, with the braided channel features being prominent in the wide valley sections. The braided river channels are characterized by a complex network of meandering channels and numerous bars and point bars. The evolution of these river bars is also highly complex. To probe the evolution patterns of bars in the typical braided river channel of the middle reaches of the Yarlung Tsangpo River, this study focuses on the Zaxi Raodeng-Paizhen braided river section in the middle and lower reaches of the Yarlung Tsangpo River. Based on 21 Landsat satellite images in November from 1986 to 2022 and utilized ArcGIS software to extract and analyze the characteristic parameters of bars within this specific river segment. In addition, the analysis incorporated morphological index and fragmentation index to examine the area characteristics and planform changes of the bars. The research findings indicate that in the Zaxi Raodeng-Paizhen river section, 80% of the bars area less than 1 km2, while only 1-3 bars area larger than 5 km², accounting for 1% to 2% of the total number of bars. Additionally, there is a significant difference in the area between bars. During the period from 1986 to 2022, the number and area of bars in the Zaxi Raodeng-Paizhen River section have shown no significant trend or abrupt change. The overall variations range are very small. Separated the study reaches at the confluence of the Niyang River to Yarlung Tsangpo River, the bars in the upstream section are more fragmented and scattered in their distribution. These bars predominantly consist of small-sized bars and the morphology of the bars tends to be short and wide. Over the years, the upstream section river dynamics have been relatively stable, and the distribution and morphology of the bars have shown small changes over time. In the downstream section of the river, the distribution of bars is relatively concentrated. These bars mainly consist of large expanse shoals, and their morphology tends to be elongated in shape. Over the years, there have been significant changes in the river's dynamics, and there have been significant changes in both the distribution and morphological characteristics of the bars. Generally, the morphology of bars in the downstream section of the river has undergone more noticeable changes compared to the upper reaches. The study has elucidated the characteristics of bars in the Zaxi Raodeng-Paizhen River section of the middle and lower reaches of the Yarlung Tsangpo River, as well as their long-term morphological changes. The conclusions drawn from this study can provide some references for river management and engineering development in the middle and lower reaches of the Yarlung Tsangpo River. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)