A Hybrid Capsule Network for Land Cover Classification Using Multispectral LiDAR Data

Yu, Yongtao; Guan, Haiyan; Li, Dilong; Gu, Tiannan; Wang, Lan-fang; Ma, Lingfei

doi:10.1109/lgrs.2019.2940505

Cited by 32 publications

(14 citation statements)

References 30 publications

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“…Therefore, it is important to prevent and repair early cracks in the pavement. However, traditional road crack detection usually relies on human inspection, limiting the accuracy and efficiency of the measurement (Li et al, 2019). Most of the common practice in the road is usually time-consuming, dangerous, labour-intensive, and subjective.…”

Section: Motivationmentioning

confidence: 99%

“…Positioning accuracy of 1 to 2 centimeters is possible with careful planning, quality hardware, favorable GPS conditions, and supplemental ground control. Additionally, MLS systems enable the mobile data collecting of the roads and constructions and provide affordable 3D databases for GIS analysis (Li et al, 2019). To be specific in the crack detection, instead of the color differences in the RGB images, the intensity differences of the generated 2D images of MLS present the crack clearly.…”

Section: Motivationmentioning

confidence: 99%

“…A geometric graph CNN was based on the MLS data established by Li et al (2019). To learn the major features from the MLS point sets, they combined the Taylor Gaussian mixture model network.…”

Section: 32mentioning

confidence: 99%

“…With this convolution, the parameter is in the size of K2*K2*256 (Sabour et al, 2017). Moreover, the second layer, called primary capsules, combines the convolutional layer features (Yu et al, 2019). Each of the primary capsules converts the data with eight convolutional kernels in the size of K3*K3*8.…”

Section: Proposed Modelmentioning

confidence: 99%

See 3 more Smart Citations

A Capsnets Approach to Pavement Crack Detection Using Mobile Laser Scannning Point Clouds

Zhu

Tan

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Routine pavement inspection is crucial to keep roads safe and reduce traffic accidents. However, traditional practices in pavement inspection are labour-intensive and time-consuming. Mobile laser scanning (MLS) has proven a rapid way for collecting a large number of highly dense point clouds covering roadway surfaces. Handling a huge amount of unstructured point clouds is still a very challenging task. In this paper, we propose an effective approach for pavement crack detection using MLS point clouds. Road surface points are first converted into intensity images to improve processing efficiency. Then, a Capsule Neural Network (CapsNet) is developed to classify the road points for pavement crack detection. Quantitative evaluation results showed that our method achieved the recall, precision, and F1-score of 95.3%, 81.1%, and 88.2% in the testing scene, respectively, which demonstrated the proposed CapsNet framework can accurately and robustly detect pavement cracks in complex urban road environments.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: 32mentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

See 2 more Smart Citations

A Capsnets Approach to Pavement Crack Detection Using Mobile Laser Scannning Point Clouds

Zhu

Tan

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Ding et al [45] designed an adaptive CapsNet composed of an adaptive routing algorithm and the powered activation regularization method for HIS classification which can amplify the gradient and learn the sparser and more discriminative representation. Yu et al [46] designed a hybrid CapsNet composed of encoder-decoder network to utilize multispectral LiDAR data and extract the high-level features for land cover classification. Guo et al [47] developed a composite multifeatured capsule network for mountainous grassland monitoring which can combine the spectral bands with extracted features.…”

Section: Introductionmentioning

confidence: 99%

Best Representation Branch Model for Remote Sensing Image Scene Classification

Zhang

Sun³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing image scene classification is an important method for understanding the high-resolution remote sensing images. Based on Convolutional Neural Network, various classification methods have been applied into this field and achieved remarkable results. These methods mainly rely on the semantic information to improve the classification performance. However, as the network goes deeper, the highly abstract and global semantic information makes it difficult for the network to accurately classify scene images with similar layout and structures, limiting the further improvement of classification accuracy. Relying on the semantic information only is not sufficient to effectively classify these similar scene images and the network needs spatial information to enhance the classification capability. To solve this dilemma, this paper proposes a best representation branch model, which reaches the optimal balance point where the network can make use of both the semantic information and spatial information to improve the final classification accuracy. In the proposed method, ResNet50 pretrained on the ImageNet dataset is first divided into four branches with different depths to extract feature maps and Capsule Network is used as the classifier. The Grad-CAM algorithm is adopted to explain the mechanism of the optimal balance point from the perspective of attention and guide the further feature fusion. In addition, ablation studies are conducted to prove the effectiveness of our method and extensive experiments are conducted on three public benchmark remote sensing datasets. The results demonstrate that the proposed method can achieve competitive classification performance compared to the state-ofthe-art methods.

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Multispectral Point Cloud Classification: A Survey

Wang,

Chen,

et al. 2024

Lecture Notes in Electrical Engineering

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A Hybrid Capsule Network for Land Cover Classification Using Multispectral LiDAR Data

Cited by 32 publications

References 30 publications

A Capsnets Approach to Pavement Crack Detection Using Mobile Laser Scannning Point Clouds

A Capsnets Approach to Pavement Crack Detection Using Mobile Laser Scannning Point Clouds

Best Representation Branch Model for Remote Sensing Image Scene Classification

Multispectral Point Cloud Classification: A Survey

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