Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method
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Elyta Widyaningrum | Roderik C. Lindenbergh | Qian Bai | Marda K. Fajari | R. Lindenbergh | E. Widyaningrum | Q. Bai | M. K. Fajari
[1] James P. Horwath,et al. Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images , 2020, npj Computational Materials.
[2] Belén Riveiro,et al. POINTNET FOR THE AUTOMATIC CLASSIFICATION OF AERIAL POINT CLOUDS , 2019, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
[3] Ryosuke Nakamura,et al. 3D semantic segmentation for high-resolution aerial survey derived point clouds using deep learning (demonstration) , 2018, SIGSPATIAL/GIS.
[4] Petteri Packalen,et al. Utility of image point cloud data towards generating enhanced multitemporal multisensor land cover maps , 2020, Int. J. Appl. Earth Obs. Geoinformation.
[5] Alaa Tharwat,et al. Classification assessment methods , 2020, Applied Computing and Informatics.
[6] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Shi Feng,et al. Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation , 2019, ICML.
[8] Yue Wang,et al. Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..
[9] Shulin Wang,et al. Feature selection in machine learning: A new perspective , 2018, Neurocomputing.
[10] Jan Dirk Wegner,et al. Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-resolution Model for Multi-class Volumetric Labeling , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] S. Schmohl,et al. SUBMANIFOLD SPARSE CONVOLUTIONAL NETWORKS FOR SEMANTIC SEGMENTATION OF LARGE-SCALE ALS POINT CLOUDS , 2019 .
[12] Alfredo Petrosino,et al. Adjusted F-measure and kernel scaling for imbalanced data learning , 2014, Inf. Sci..
[13] Weimin Wang,et al. A Point-Wise LiDAR and Image Multimodal Fusion Network (PMNet) for Aerial Point Cloud 3D Semantic Segmentation , 2019, Remote. Sens..
[14] Jan Boehm,et al. A review on deep learning techniques for 3D sensed data classification , 2019, Remote. Sens..
[15] Pascual Campoy Cervera,et al. A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles , 2017, J. Sensors.
[16] Joaquín Martínez-Sánchez,et al. Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data , 2019, Sensors.
[17] Russell G. Congalton,et al. A review of assessing the accuracy of classifications of remotely sensed data , 1991 .
[18] C. Qi. Deep Learning on Point Sets for 3 D Classification and Segmentation , 2016 .
[19] Bastian Leibe,et al. Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
[20] Sebastian Raschka,et al. An Overview of General Performance Metrics of Binary Classifier Systems , 2014, ArXiv.
[21] Giles M. Foody,et al. Status of land cover classification accuracy assessment , 2002 .
[22] Norbert Pfeifer,et al. Classification of ALS Point Clouds Using End-to-End Deep Learning , 2019, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.
[23] David Masko,et al. The Impact of Imbalanced Training Data for Convolutional Neural Networks , 2015 .
[24] Anh Nguyen,et al. 3D point cloud segmentation: A survey , 2013, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM).
[25] Martin Simonovsky,et al. Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Md. Zakirul Alam Bhuiyan,et al. A Survey on Deep Learning in Big Data , 2017, 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).
[27] Franz Rottensteiner,et al. Building and Road Extraction by LiDAR and Imagery , 2017 .
[28] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Taghi M. Khoshgoftaar,et al. Survey on deep learning with class imbalance , 2019, J. Big Data.
[30] Rong Huang,et al. Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global , 2020 .
[31] Yaping Lin,et al. Using Training Samples Retrieved from a Topographic Map and Unsupervised Segmentation for the Classification of Airborne Laser Scanning Data , 2020, Remote. Sens..
[32] T. B. Alakus,et al. Comparison of deep learning approaches to predict COVID-19 infection , 2020, Chaos, Solitons & Fractals.
[33] Wisnu Jatmiko,et al. Semantic Segmentation on LiDAR Point Cloud in Urban Area using Deep Learning , 2019, 2019 International Workshop on Big Data and Information Security (IWBIS).
[34] Sarah Filippi,et al. Interpreting Deep Neural Networks Through Variable Importance , 2019, ArXiv.
[35] Xiao Xiang Zhu,et al. A Review of Point Cloud Semantic Segmentation , 2019 .
[36] Juntao Yang,et al. A probabilistic graphical model for the classification of mobile LiDAR point clouds , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[37] Lei Zhang,et al. A deep manifold learning approach for spatial-spectral classification with limited labeled training samples , 2019, Neurocomputing.
[38] Wei Wu,et al. PointCNN: Convolution On X-Transformed Points , 2018, NeurIPS.