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Keywords = tenor sparse coding

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Article
Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points
by Nan Li, Norbert Pfeifer and Chun Liu
Remote Sens. 2017, 9(12), 1216; https://doi.org/10.3390/rs9121216 - 27 Nov 2017
Cited by 9 | Viewed by 6209
Abstract
The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. In many methods, only the features of each point are used, regardless of their spatial distribution within a [...] Read more.
The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. In many methods, only the features of each point are used, regardless of their spatial distribution within a certain neighborhood. This paper proposes a tensor-based sparse representation classification (TSRC) method for airborne LiDAR (Light Detection and Ranging) points. To keep features arranged in their spatial arrangement, each LiDAR point is represented as a 4th-order tensor. Then, TSRC is performed for point classification based on the 4th-order tensors. Firstly, a structured and discriminative dictionary set is learned by using only a few training samples. Subsequently, for classifying a new point, the sparse tensor is calculated based on the tensor OMP (Orthogonal Matching Pursuit) algorithm. The test tensor data is approximated by sub-dictionary set and its corresponding subset of sparse tensor for each class. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on eight real LiDAR point clouds whose result shows that objects can be distinguished by TSRC successfully. The overall accuracy of all the datasets is beyond 80% by TSRC. TSRC also shows a good improvement on LiDAR points classification when compared with other common classifiers. Full article
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Full article ">Figure 1
<p>The tensor generation from a point cloud set procedure.</p> Full article ">Figure 2
<p>Tensor-based sparse representation classification procedure.</p> Full article ">Figure 3
<p>The reconstruction residuals of dictionary set of each iteration.</p> Full article ">Figure 4
<p>Height difference feature results: (<b>a</b>) Height difference via constant scale neighborhood; (<b>b</b>) Height difference via multiple scale neighborhood.</p> Full article ">Figure 5
<p>The feature extraction results of Dataset 3: (<b>a</b>) Height difference; (<b>b</b>) NormalZ; (<b>c</b>) NormalSigma0; (<b>d</b>) Echo Ratio; (<b>e</b>) Echo number ratio; and, (<b>f</b>) Eigenentropy.</p> Full article ">Figure 6
<p>Three-dimensional view of the classification results of eight datasets using TSRC.</p> Full article ">Figure 7
<p>Qualification of the classification over eight datasets.</p> Full article ">Figure 8
<p>Accuracy per class comparisons of tensor-based sparse representation classification (TSRC) and other classifiers: (<b>a</b>) Overall accuracy; (<b>b</b>) Open ground accuracy; (<b>c</b>) Vegetation accuracy; (<b>d</b>) Roof accuracy; (<b>e</b>) Covered ground accuracy; (<b>f</b>) Facade accuracy.</p> Full article ">Figure 8 Cont.
<p>Accuracy per class comparisons of tensor-based sparse representation classification (TSRC) and other classifiers: (<b>a</b>) Overall accuracy; (<b>b</b>) Open ground accuracy; (<b>c</b>) Vegetation accuracy; (<b>d</b>) Roof accuracy; (<b>e</b>) Covered ground accuracy; (<b>f</b>) Facade accuracy.</p> Full article ">Figure 9
<p>The OA of dataset 3 with different amount of training tensors by different classifiers.</p> Full article ">
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