Mr. Niansheng Chen Profile

Niansheng Chen

at Shanghai Dianji Univ

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Publications (3)

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SPIE Journal Paper | 8 November 2024

WatNet: a high-precision water body extraction method in remote sensing images under complex backgrounds

Qianqian Yang, Lei Rao, Guangyu Fan, Niansheng Chen, Songlin Cheng, Xiaoyong Song, Dingyu Yang

JARS, Vol. 18, Issue 04, 044515, (November 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.044515

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Water body extraction from remote sensing images in complex backgrounds is crucial for environmental monitoring, disaster management, and urban planning. Although existing water body extraction algorithms for remote sensing images offer robust tools for monitoring, they still face challenges, including the inability to extract fine water bodies and the frequent omission of water body edges in complex backgrounds such as dense vegetation, varying terrain, or cloud interference. In response, we introduce an architecture named WatNet to enhance the precision of water body extraction in complex environments. WatNet comprises three main modules: the global multi-attention fusion module (GMAF), the water forward network module (WFN), and the edge focus attention module (EFA). The GMAF module enhances the model’s capability to capture global information through multi-head self-attention and convolutional attention modules, improving the overall feature extraction of water bodies. The WFN module utilizes depth-wise separable convolution and attention mechanisms to enhance the capture of local features in fine water bodies. The EFA module significantly improves the clarity and accuracy of water body boundaries through refined edge detection. Experiments on the LoveDA Remote Sensing Land Cover (LoveDA), Qinghai–Tibet Plateau (QTPL), and Wuhan dense labeling (WHDLD) datasets show that WatNet outperforms the mainstream methods in precision, recall, overall accuracy (OA), F1 score, and mean Intersection over Union (mIoU). On the LoveDA dataset, WatNet’s mIoU improved by 1.24% compared with the second-best method, by 0.4% on the QTPL dataset, and by 0.51% on the WHDLD dataset. These results validate the WatNet’s effectiveness and robustness in water body extraction tasks under various environmental conditions.

Proceedings Article | 7 May 2024 Paper

MAPF-LNS2* algorithm based on fast repair and parallelization

Wenhao Geng, Niansheng Chen, Xiaoyong Song, Songlin Cheng

Proceedings Volume 13161, 131610K (2024) https://doi.org/10.1117/12.3025682

KEYWORDS: Switches, Reflection, Unmanned aerial vehicles, Tunable filters, Robotic systems, Iris, Formation flying, Failure analysis, Education and training, Design

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Aiming at the problem of time loss caused by weight distribution imbalance in ALNS module of MAPF-LNS2 algorithm, ALNS+ module is proposed by introducing improvement rate statistics, time window, improvement rate trend judgment function and other mechanisms. Compared with the ALNS module, the ALNS+ module considers the recent improvement rate trend of the neighborhood search strategy, and switches to other neighborhood search strategies when the trend decreases, so as to repair the excessive weight allocation in time and reduce the time loss. The experimental results show that the ALNS+ module achieves a significant improvement in running time, and the maximum reduction is 65.1%. However, in scenarios such as denser agents, the execution success rate of the improved module has a significant downward trend. The PNS module was proposed to speed up the neighborhood repair process by introducing a parallelization method to parallel the neighborhood search process on multiple processor cores. The comparative experimental results show that the PNS module has a significant improvement in the success rate, with a maximum increase of 40%. By integrating the ALNS+ and PNS modules into the MAPF-LNS2 algorithm framework, the MAPF-LNS2* algorithm was proposed. The simulation results show that the MAPF-LNS2* algorithm effectively solves the problem of time loss in the MAPF-LNS2 algorithm, and effectively reduces the failure rate in the agent dense scene.

Proceedings Article | 16 March 2023 Paper

Visual prior map assisted monocular location algorithm based on 3D spatial lines

Yuchen Gong, Lei Rao, Guangyu Fan, Niansheng Chen, Xiaoyong Song, Songlin Cheng, Dingyu Yang

Proceedings Volume 12593, 125930M (2023) https://doi.org/10.1117/12.2671455

KEYWORDS: Visualization, Cameras, LIDAR, 3D image processing, Pose estimation, Imaging systems, Feature extraction, Image processing, 3D tracking, Mobile robots

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When the Visual-Inertial Odometry (VIO) is started, its Inertial Measurement Unit (IMU) lacks acceleration incentive, which will result in poor orientation estimation accuracy during initialization, or even initialization failure. Therefore, a visual priori map-assisted monocular location algorithm based on 3D spatial straight lines is proposed. Firstly, the monocular image data of the surrounding environment were extracted through the Line Segment Detection algorithm (LSD), and high precision 2D line features were selected according to the length of the line and the number of surrounding point features. The 3D spatial lines of the surrounding environment were obtained using the line and surface intersection method. Construct a visual prior map with 3D spatial straight lines. Secondly, the constructed visual prior map is used as the online monocular VIO pose estimation for the global map. Based on the straight-line feature matching algorithm and the 3D space straight line depth information as additional constraints, the 2D straight-line feature in the monocular VIO's current field of vision is matched with the 3D space straight line in the visual prior map. The matching results were used as global constraints to optimize the monocular VIO pose. Tests on EUROC and TUM common data sets show that the 3D spatial straight line based visual prior map can effectively correct the pose during the monocular VIO initialization stage. Compared with the VINS-Mono localization algorithm, this algorithm can effectively improve the pose estimation accuracy during VIO initialization and reduce the overall trajectory positioning error.

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