Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR

Tan

Pei

et al. 2018

Sensors

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

Section: Resultsmentioning

confidence: 99%

Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Tan

Pei

et al. 2018

Sensors

“…The HM_1 [36] uses conditional random fields (CRF) to perform the semantic analysis of the ALS data. The UM [37] uses a genetic algorithm based on the features extracted from the LiDAR point-attributes, textural analysis, and geometric attributes for the 3D semantic labeling. The LUH [38] uses hierarchical conditional random fields (HCRF) for the classification.…”

Section: Isprs Benchmark Testing Resultsmentioning

confidence: 99%

A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds

Jiang

et al. 2017

Remote Sensing

3D semantic labeling is a fundamental task in airborne laser scanning (ALS) point clouds processing. The complexity of observed scenes and the irregularity of point distributions make this task quite challenging. Existing methods rely on a large number of features for the LiDAR points and the interaction of neighboring points, but cannot exploit the potential of them. In this paper, a convolutional neural network (CNN) based method that extracts the high-level representation of features is used. A point-based feature image-generation method is proposed that transforms the 3D neighborhood features of a point into a 2D image. First, for each point in the ALS data, the local geometric features, global geometric features and full-waveform features of its neighboring points within a window are extracted and transformed into an image. Then, the feature images are treated as the input of a CNN model for a 3D semantic labeling task. Finally, to allow performance comparisons with existing approaches, we evaluate our framework on the publicly available datasets provided by the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) benchmark tests on 3D labeling. The experiment results achieve 82.3% overall accuracy, which is the best among all considered methods.

“…The proposed method were compared with five state-of-the-art methods, including UM, LUH, NANJ2, WhuY3, and BIJ_W. The UM method employs supervised machine learning for point cloud classification, and the features are extracted from point-attributes, textural analysis, and geometric attributes [43]. The LUH method [26] uses a hierarchical framework based on conditional random fields and integrates object features by voxel cloud connectivity segmentation [44] for classification.…”

Section: Resultsmentioning

confidence: 99%

Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas

Wang

et al. 2019

Sensors

Classifying the LiDAR (Light Detection and Ranging) point cloud in the urban environment is a challenging task. Due to the complicated structures of urban objects, it is difficult to find suitable features and classifiers to efficiently category the points. A two-layered graph-cuts-based classification framework is addressed in this study. The hierarchical framework includes a bottom layer that defines the features and classifies point clouds at the point level as well as a top layer that defines the features and classifies the point cloud at the object level. A novel adaptive local modification method is employed to model the interactions between these two layers. The iterative graph cuts algorithm runs around the bottom and top layers to optimize the classification. In this way, the addressed framework benefits from the integration of point features and object features to improve the classification. The experiments demonstrate that the proposed method is capable of producing classification results with high accuracy and efficiency.