A Novel Rapid Method for Viewshed Computation on DEM through Max-Pooling and Min-Expected Height

Pan, Zhibin; Tang, Jin; Tjahjadi, Tardi; Wu, Zhihu; Xiao, Xiaoming

doi:10.3390/ijgi9110633

Cited by 4 publications

(2 citation statements)

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“…Finally, the model also exhibits optimization in detail, such as the choice between Maxpool and Avgpool [30]. Because the objects of ancient mural segmentation models prefer texture contour features, maximum pooling is selected as the pooling method of the model, which filters image irrelevant feature information, and thus, makes the mural segmentation effect more distinct.…”

Section: Psp-m Modelmentioning

confidence: 99%

Ancient mural segmentation based on a deep separable convolution network

et al. 2022

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Traditional methods for ancient mural segmentation have drawbacks, including fuzzy target boundaries and low efficiency. Targeting these problems, this study proposes a pyramid scene parsing MobileNetV2 network (PSP-M) by fusing a deep separable convolution-based lightweight neural network with a multiscale image segmentation model. In this model, deep separable convolution-fused MobileNetV2, as the backbone network, is embedded in the image segmentation model, PSPNet. The pyramid scene parsing structure, particularly owned by the two models, is used to process the background features of images, which aims to reduce feature loss and to improve the efficiency of image feature extraction. In the meantime, atrous convolution is utilized to expand the perceptive field, aiming to ensure the integrity of image semantic information without changing the number of parameters. Compared with traditional image segmentation models, PSP-M increases the average training accuracy by 2%, increases the peak signal-to-noise ratio by 1–2 dB and improves the structural similarity index by 0.1–0.2.

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Section: Psp-m Modelmentioning

confidence: 99%

Ancient mural segmentation based on a deep separable convolution network

et al. 2022

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“…Filtering methods based on airborne LiDAR data can mainly be categorized as traditional and machine learning methods. Due to the complex spatial structure and diverse morphology of terrains, the main idea of traditional filtering methods is to construct terrain models at different scales and gradually refine the terrain to achieve better representation of terrain features [10][11][12][13]. However, they are often affected by factors such as threshold settings, data transformation losses, and operator errors.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Global Relationship via the Point Cloud Transformer for the Terrain Filtering of Airborne LiDAR Data

Cheng,

Hao,

Cheng

et al. 2023

Remote Sensing

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Due to the irregularity and complexity of ground and non-ground objects, filtering non-ground data from airborne LiDAR point clouds to create Digital Elevation Models (DEMs) remains a longstanding and unresolved challenge. Recent advancements in deep learning have offered effective solutions for understanding three-dimensional semantic scenes. However, existing studies lack the capability to model global semantic relationships and fail to integrate global and local semantic information effectively, which are crucial for the ground filtering of point cloud data, especially for larger objects. This study focuses on ground filtering challenges in large scenes and introduces an elevation offset-attention (E-OA) module, which considers global semantic features and integrates them into existing network frameworks. The performance of this module has been validated on three classic benchmark models (RandLA-Net, point transformer, and PointMeta-L). It was compared with two traditional filtering methods and the advanced CDFormer model. Additionally, the E-OA module was compared with three state-of-the-art attention frameworks. Experiments were conducted on two distinct data sources. The results show that our proposed E-OA module improves the filtering performance of all three benchmark models across both data sources, with a maximum improvement of 6.15%. The performance of models was enhanced with the E-OA module, consistently exceeding that of traditional methods and all competing attention frameworks. The proposed E-OA module can serve as a plug-and-play component, compatible with existing networks featuring local feature extraction capabilities.

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