Semantic Segmentation of LiDAR Points Clouds: Rasterization Beyond Digital Elevation Models

Guiotte, Florent; Pham, Minh‐Tan; Dambreville, Romain; Corpetti, Thomas; Lefèvre, Sébastien

doi:10.1109/lgrs.2019.2958858

Cited by 18 publications

(14 citation statements)

References 10 publications

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“…More recently, several semantic segmentation methods have been proposed specifically to deal with specific aspects of remote sensing images such as spatial constraints [28,26,27,42,2,29] or non-RGB data [17,13]. Nogueira et al [28] use patchwise semantic segmentation in RS imaging for both urban and agricultural scenarios.…”

Section: Cnnmentioning

confidence: 99%

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Fully Convolutional Open Set Segmentation

Oliveira¹,

Silva²,

Machado³

et al. 2020

Preprint

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In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches. However, these methods trained in a Closed Set of classes fail when new classes are found in the test phase. It means that they are not suitable for Open Set scenarios, which are very common in real-world computer vision and remote sensing applications. In this paper, we discuss the limitations of Closed Set segmentation and propose two fully convolutional approaches to effectively address Open Set semantic segmentation: OpenFCN and OpenPCS. OpenFCN is based on the wellknown OpenMax algorithm, configuring a new application of this approach in segmentation settings. OpenPCS is a fully novel approach based on feature-space from DNN activations that serve as features for computing PCA and multi-variate gaussian likelihood in a lower dimensional space. Experiments were conducted on the well-known Vaihingen and Potsdam segmentation datasets. OpenFCN showed little-to-no improvement when compared to the simpler and much more time efficient SoftMax thresholding, while being between some orders of magnitude slower. OpenPCS achieved promising results in almost all experiments by overcoming both OpenFCN and SoftMax thresholding. OpenPCS is also a reasonable compromise between the runtime performances of the extremely fast SoftMax thresholding and the extremely slow OpenFCN, being close able to run close to real-time. Experiments also indicate that OpenPCS is effective, robust and suitable for Open Set segmentation, being able to improve the recognition of unknown class pixels without reducing the accuracy on the known class pixels.

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Section: Cnnmentioning

confidence: 99%

“…In [17], the authors adapt state-of-the-art semantic segmentation approaches to work with multi-spectral images. Guiote et al [13] proposed an aprooach for semantic segmentation from LiDAR point clouds.…”

Section: Cnnmentioning

confidence: 99%

Fully Convolutional Open Set Segmentation

Oliveira¹,

Silva²,

Machado³

et al. 2020

Preprint

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“…Many DL models are adapted well to structured data such as images or videos. Therefore, it is advantageous to create regular raster grids such as DEMs from the ALS point clouds which could be fed to DL models for training (Guiotte et al, 2020). Values represented by DEM cells however show either absolute distance from the terrain to the acquisition device or relative elevations based on a reference surface, and in cases where the shape of objects and structures are relevant regardless of how high or low of a terrain they are located at, only elevations relative to neighboring cells matter.…”

Section: Introductionmentioning

confidence: 99%

Detection of Terrain Structures in Airborne Laser Scanning Data Using Deep Learning

Kazimi

Thiemann

Sester

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Automated recognition of terrain structures is a major research problem in many application areas. These structures can be investigated in raster products such as Digital Elevation Models (DEMs) generated from Airborne Laser Scanning (ALS) data. Following the success of deep learning and computer vision techniques on color images, researchers have focused on the application of such techniques in their respective fields. One example is detection of structures in DEM data. DEM data can be used to train deep learning models, but recently, Du et al. (2019) proposed a multi-modal deep learning approach (hereafter referred to as MM) proving that combination of geomorphological information help improve the performance of deep learning models. They reported that combining DEM, slope, and RGB-shaded relief gives the best result among other combinations consisting of curvature, flow accumulation, topographic wetness index, and grey-shaded relief. In this work, we approve and build on top of this approach. First, we use MM and show that combinations of other information such as sky view factors, (simple) local relief models, openness, and local dominance improve model performance even further. Secondly, based on the recently proposed HR-Net (Sun et al., 2019), we build a tinier, Multi-Modal High Resolution network called MM-HR, that outperforms MM. MM-HR learns with fewer parameters (4 millions), and gives an accuracy of 84:2 percent on ZISM50m data compared to 79:2 percent accuracy by MM which learns with more parameters (11 millions). On the dataset of archaeological mining structures from Harz, the top accuracy by MM-HR is 91:7 percent compared to 90:2 by MM.

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“…Because of their ability to capture complex structures, many domains related to geosciences and earth observation are making increasing use of LiDAR data. Such systems provide indeed accurate 3D point clouds of the scanned scene which has a large number of applications ranging from urban scene analysis (Chehata et al, 2009, Guiotte et al, 2020, Shan, Aparajithan, 2005, geology and erosion (Brodu, Lague, 2012), archaeology (Witharana et al, 2018) or even ecology (Eitel et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…While first works have been focused on the characterization of single points (often through height and intensity) without including information related to their neighbours (Lodha et al, 2006), more advanced approaches have included spatial relationships using a set of spheres or cylinders (of variable radius) around each point to extract consistent geometric features (Mallet et al, 2011, Weinmann et al, 2015, Niemeyer et al, 2014. Among others, we have demonstrated in (Guiotte et al, 2019b, Guiotte et al, 2020 that the various rasterization strategies may have an important impact on the final result.…”

Section: Introductionmentioning

confidence: 99%

Relation Network for Full-Waveforms Lidar Classification

Guiotte

Rao

Lefèvre

et al. 2020

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

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Abstract. LiDAR data are widely used in various domains related to geosciences (flow, erosion, rock deformations, etc.), computer graphics (3D reconstruction) or earth observation (detection of trees, roads, buildings, etc.). Because of the unstructured nature of remaining 3D points and because of the cost of acquisition, the LiDAR data processing is still challenging (few learning data, difficult spatial neighboring relationships, etc.). In practice, one can directly analyze the 3D points using feature extraction and then classify the points via machine learning techniques (Brodu, Lague, 2012, Niemeyer et al., 2014, Mallet et al., 2011). In addition, recent neural network developments have allowed precise point cloud segmentation, especially using the seminal pointnet network and its extensions (Qi et al., 2017a, Riegler et al., 2017). Other authors rather prefer to rasterize / voxelize the point cloud and use more conventional computers vision strategies to analyze structures (Lodha et al., 2006). In a recent work, we demonstrated that Digital Elevation Models (DEM) is reductive of the vertical component complexity describing objects in urban environments (Guiotte et al., 2020). These results highlighted the necessity to preserve the 3D structure of the point cloud as long as possible in the processing. In this paper, we therefore rely on ortho-waveforms to compute a land cover map. Ortho-waveforms are directly computed from the waveforms in a regular 3D grid. This method provides volumes somehow “similar” to hyperspectral data where each pixel is here associated with one ortho-waveform. Then, we exploit efficient neural networks adapted to the classification of hyperspectral data when few samples are available. Our results, obtained on the 2018 Data Fusion Contest dataset (DFC), demonstrate the efficiency of the approach.

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Semantic Segmentation of LiDAR Points Clouds: Rasterization Beyond Digital Elevation Models

Cited by 18 publications

References 10 publications

Fully Convolutional Open Set Segmentation

Fully Convolutional Open Set Segmentation

Detection of Terrain Structures in Airborne Laser Scanning Data Using Deep Learning

Relation Network for Full-Waveforms Lidar Classification

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