ALS Point Cloud Classification With Small Training Data Set Based on Transfer Learning

Abstract. Airborne Light Detection And Ranging (LiDAR) has become an important means for efficient and high-precision acquisition of 3D spatial data of large scenes. It has important application value in digital cities and location-based services. The classification and identification of point cloud is the basis of its application, and it is also a hot and difficult problem in the field of geographic information science.The difficulty of LiDAR point cloud classification in large-scale urban scenes is: On the one hand, the urban scene LiDAR point cloud contains rich and complex features, many types of features, different shapes, complex structures, and mutual occlusion, resulting in large data loss; On the other hand, the LiDAR scanner is far away from the urban features, and is like a car, a pedestrian, etc., which is in motion during the scanning process, which causes a certain degree of data noise of the point cloud and uneven density of the point cloud.Aiming at the characteristics of LiDAR point cloud in urban scene.The main work of this paper implements a method based on the saliency dictionary and Latent Dirichlet Allocation (LDA) model for LiDAR point cloud classification. The method uses the tag information of the training data and the tag source of each dictionary item to construct a significant dictionary learning model in sparse coding to expresses the feature of the point set more accurately.And it also uses the multi-path AdaBoost classifier to perform the features of the multi-level point set. The classification of point clouds is realized based on the supervised method. The experimental results show that the feature set extracted by the method combined with the multi-path classifier can significantly improve the cloud classification accuracy of complex city market attractions.

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SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-w1-91-2017 ◽

2017 ◽

Vol IV-1/W1 ◽

pp. 91-98 ◽

Cited By ~ 88

Author(s):

T. Hackel ◽

N. Savinov ◽

L. Ladicky ◽

J. D. Wegner ◽

K. Schindler ◽

...

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Point Clouds ◽

Full Potential ◽

3D Point Cloud ◽

Learning Methods ◽

Data Set ◽

Cloud Classification ◽

Wide Range ◽

Point Cloud Classification

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

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A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification

Mathematics ◽

10.3390/math9121328 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1328

Author(s):

Manuel Oviedo-de la Fuente ◽

Carlos Cabo ◽

Celestino Ordóñez ◽

Javier Roca-Pardiñas

Keyword(s):

Point Cloud ◽

Multinomial Logistic Regression ◽

Machine Learning Algorithms ◽

Support Vector ◽

Dc Functions ◽

Data Set ◽

Distance Correlation ◽

3D Point Clouds ◽

Cloud Classification ◽

Point Cloud Classification

Supervised classification of 3D point clouds using machine learning algorithms and handcrafted local features as covariates frequently depends on the size of the neighborhood (scale) around each point used to determine those features. It is therefore crucial to estimate the scale or scales providing the best classification results. In this work, we propose three methods to estimate said scales, all of them based on calculating the maximum values of the distance correlation (DC) functions between the features and the label assigned to each point. The performance of the methods was tested using simulated data, and the method presenting the best results was applied to a benchmark data set for point cloud classification. This method consists of detecting the local maximums of DC functions previously smoothed to avoid choosing scales that are very close to each other. Five different classifiers were used: linear discriminant analysis, support vector machines, random forest, multinomial logistic regression and multilayer perceptron neural network. The results obtained were compared with those from other strategies available in the literature, being favorable to our approach.

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