Full-Waveform Airborne LiDAR Data Classification Using Convolutional Neural Networks

Zorzi, Stefano; Maset, Eleonora; Fusiello, Andrea; Crosilla, Fabio

doi:10.1109/tgrs.2019.2919472

Cited by 34 publications

(39 citation statements)

References 15 publications

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“…1). Similarly to the algorithm proposed in (Zorzi et al, 2019), our method is composed of two main stages ( Fig. 2 Figure 1.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…In the last years, several FCN models have been proposed to solve semantic segmentation (Ciresan et al, 2012, Garcia-Garcia et al, 2017. For our application, we started from the popular U-net architecture (Ronneberger et al, 2015), already applied also in (Zorzi et al, 2019) and specifically adapted to segment the four-channel images created as previously described.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Leveraging on the high performances that can be obtained by the networks usually applied for image processing, (Zorzi et al, 2019) proposed to map the point cloud and the information derived from full-waveform data into an image segmented by a FCN, assigning in this way a label to each pixel and, consequently, to the point falling in the pixel. A similar approach was applied also in (Rizaldy et al, 2018): in this case, the distinction among the three classes ground, vegetation and building is achieved with an overall accuracy of 93%.…”

Section: State Of the Artmentioning

confidence: 99%

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Efficient Large-Scale Airborne Lidar Data Classification via Fully Convolutional Network

Maset

Padova

Fusiello

2020

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

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Abstract. Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700 km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (ground, vegetation, roof, overground and power line), with an overall accuracy of 92.9%.

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“…1). Similarly to the algorithm proposed in (Zorzi et al, 2019), our method is composed of two main stages ( Fig. 2 Figure 1.…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Large-Scale Airborne Lidar Data Classification via Fully Convolutional Network

Maset

Padova

Fusiello

2020

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

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“…Maset et al (2015) offered a data-driven alternative method, to solve the unsupervised classification task of waveform without calculating some handcrafted features, or converting into other data structures like image using self-organizing maps (SOMs) [ 25 ]. Additionally, Zorzi et al (2019) [ 26 ] proposed a deep-learning based data-driven classification approach for full-waveform lidar data, which consist of point clouds with associated entire waveforms. In this method, classification tasks are divided into two steps.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, this method requires a large assumption that there is no occlusion, because it is projected onto a two-dimensional image. However, Zorzi et al [ 26 ] demonstrated the necessity of using the spatial learning method for full-waveform lidar data via 2D FCN. Moreover, Shinohara et al (2019) showed the effectiveness of applying the spatial learning method to full-waveform lidar data using a full-waveform network auto encoder (FWNetAE), consisting of a PointNet [ 7 ] based encoder and a naïve multi-layer-perceptron based decoder [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning

Shinohara

Xiu

Matsuoka

2020

Sensors

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In the computer vision field, many 3D deep learning models that directly manage 3D point clouds (proposed after PointNet) have been published. Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised learning tasks on 3D point cloud data, such as classification and segmentation tasks for open datasets in competitions. Furthermore, many researchers have attempted to apply these deep learning-based techniques to 3D point clouds observed by aerial laser scanners (ALSs). However, most of these studies were developed for 3D point clouds without radiometric information. In this paper, we investigate the possibility of using a deep learning method to solve the semantic segmentation task of airborne full-waveform light detection and ranging (lidar) data that consists of geometric information and radiometric waveform data. Thus, we propose a data-driven semantic segmentation model called the full-waveform network (FWNet), which handles the waveform of full-waveform lidar data without any conversion process, such as projection onto a 2D grid or calculating handcrafted features. Our FWNet is based on a PointNet-based architecture, which can extract the local and global features of each input waveform data, along with its corresponding geographical coordinates. Subsequently, the classifier consists of 1D convolutional operational layers, which predict the class vector corresponding to the input waveform from the extracted local and global features. Our trained FWNet achieved higher scores in its recall, precision, and F1 score for unseen test data—higher scores than those of previously proposed methods in full-waveform lidar data analysis domain. Specifically, our FWNet achieved a mean recall of 0.73, a mean precision of 0.81, and a mean F1 score of 0.76. We further performed an ablation study, that is assessing the effectiveness of our proposed method, of the above-mentioned metric. Moreover, we investigated the effectiveness of our PointNet based local and global feature extraction method using the visualization of the feature vector. In this way, we have shown that our network for local and global feature extraction allows training with semantic segmentation without requiring expert knowledge on full-waveform lidar data or translation into 2D images or voxels.

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Transmission Tower Classification Using Point Cloud Similarity

Silva¹,

Amaro²

2022

Lecture Notes in Electrical Engineering

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Full-Waveform Airborne LiDAR Data Classification Using Convolutional Neural Networks

Cited by 34 publications

References 15 publications

Efficient Large-Scale Airborne Lidar Data Classification via Fully Convolutional Network

Efficient Large-Scale Airborne Lidar Data Classification via Fully Convolutional Network

FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning

Transmission Tower Classification Using Point Cloud Similarity

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