Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud

Hu, Xiangyun; Yuan, Yi

doi:10.3390/rs8090730

Cited by 140 publications

(121 citation statements)

References 17 publications

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“…Examples of other feature sets used in the point classification context are Fast Point Feature Histogram (FPFH) (Rusu et al, 2009) or Color Signature of Histogram of Orientations (SHOT) (Tombari et al, 2010). All these methods use handcrafted features that can be considered suboptimal when compared to more recent deep learning techniques (Hu andYuan, 2016, Qi et al, 2016), which learn features directly on image or point cloud data. Those approaches have not been considered here, since they require large computational power to train the classifier, and may be restrictive at prediction time, depending on the hardware available.…”

Section: Related Workmentioning

confidence: 99%

Classification of Aerial Photogrammetric 3d Point Clouds

Becker

Häni

Rosinskaya

et al. 2017

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

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ABSTRACT:We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

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Section: Related Workmentioning

confidence: 99%

Classification of Aerial Photogrammetric 3d Point Clouds

Becker

Häni

Rosinskaya

et al. 2017

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

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“…Following the popularity of deep learning, a CNN-based technique was proposed to be used to classify point clouds into ground and non-ground for DTM generation [11]. The method achieved lower error rates compared to other filtering algorithms in an ISPRS (International Society for Photogrammetry and Remote Sensing) filter test dataset [11]. The ISPRS filter test dataset is a benchmark light detection and ranging (LIDAR) dataset for analyzing the performance of filtering algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…We use point-to-image conversion following the approach adopted in Hu and Yuan [11]. However, our method converts all of the points into a multi-dimensional image to accelerate the computational time.…”

Section: Introductionmentioning

confidence: 99%

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Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks

et al. 2018

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Various classification methods have been developed to extract meaningful information from Airborne Laser Scanner (ALS) point clouds. However, the accuracy and the computational efficiency of the existing methods need to be improved, especially for the analysis of large datasets (e.g., at regional or national levels). In this paper, we present a novel deep learning approach to ground classification for Digital Terrain Model (DTM) extraction as well as for multi-class land-cover classification, delivering highly accurate classification results in a computationally efficient manner. Considering the top-down acquisition angle of ALS data, the point cloud is initially projected on the horizontal plane and converted into a multi-dimensional image. Then, classification techniques based on Fully Convolutional Networks (FCN) with dilated kernels are designed to perform pixel-wise image classification. Finally, labels are transferred from pixels to the original ALS points. We also designed a Multi-Scale FCN (MS-FCN) architecture to minimize the loss of information during the point-to-image conversion. In the ground classification experiment, we compared our method to a Convolutional Neural Network (CNN)-based method and LAStools software. We obtained a lower total error on both the International Society for Photogrammetry and Remote Sensing (ISPRS) filter test benchmark dataset and AHN-3 dataset in the Netherlands. In the multi-class classification experiment, our method resulted in higher precision and recall values compared to the traditional machine learning technique using Random Forest (RF); it accurately detected small buildings. The FCN achieved precision and recall values of 0.93 and 0.94 when RF obtained 0.91 and 0.92, respectively. Moreover, our strategy significantly improved the computational efficiency of state-of-the-art CNN-based methods, reducing the point-to-image conversion time from 47 h to 36 min in our experiments on the ISPRS filter test dataset. Misclassification errors remained in situations that were not included in the training dataset, such as large buildings and bridges, or contained noisy measurements.As a result, points' features are represented by pixel values in the extracted image. Consequently, the point classification task is transformed to a pixel-wise image classification task. To address this task, we introduce a Fully Convolutional Network (FCN), which is a CNN variant that can predict the classification labels of every pixel in the image directly. We adopt FCN with dilated kernel (FCN-DK) for the classification [15]. FCN-DK is a no down-sampling network architecture that maintains the spatial size of the feature maps of each layer to be the same as the input. It uses dilated kernels to capture larger spatial contextual information, and therefore increases the receptive field of the network without increasing the number of parameters. We modify the FCN-DK network to perform ground and multi-class classification of an ALS point cloud. We also propose Multi-Scale FCN (MS-FCN) architectu...

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“…The back propagation procedure simply adopts the chain rule derivative [69], this is achieved where the gradient of the objective with respect to the input module, is computed backwards from the output module [69,70]. This was considered due to its performance in updating the weight and bias values according to the scaled conjugate gradient; the training stops when certain conditions are met such as the maximum number of epochs is reached, maximum amount of time is exceeded, performance is minimized to the goal and the validation performance has increased more than the maximum it recorded [65].…”

Section: Learningmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

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Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud

Cited by 140 publications

References 17 publications

Classification of Aerial Photogrammetric 3d Point Clouds

Classification of Aerial Photogrammetric 3d Point Clouds

Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

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