A Two-Step Feature Extraction Algorithm

A Two-Step Feature Extraction Algorithm: Application to deep learning for point cloud classification

Nurunnabi, A. (University of Luxembourg)
Teferle, F. N. (University of Luxembourg)
Laefer, D. F. (New York University)
Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing)
Hunegnaw, A. (University of Luxembourg)

2022

Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally intensive and vulnerable to overfitting. Furthermore, reliance on empirically-based feature dimensionality reduction may lead to misclassification. In contrast, efficient feature management can reduce storage and computational complexities, builds better classifiers, and improves overall performance. Principal Component Analysis (PCA) is a well-known dimension reduction technique that has been used for feature extraction. This paper presents a two-step PCA based feature extraction algorithm that employs a variant of feature-based PointNet (Qi et al., 2017a) for point cloud classification. This paper extends the PointNet framework for use on large-scale aerial LiDAR data, and contributes by (i) developing a new feature extraction algorithm, (ii) exploring the impact of dimensionality reduction in feature extraction, and (iii) introducing a non-end-to-end PointNet variant for per point classification in point clouds. This is demonstrated on aerial laser scanning (ALS) point clouds. The algorithm successfully reduces the dimension of the feature space without sacrificing performance, as benchmarked against the original PointNet algorithm. When tested on the well-known Vaihingen data set, the proposed algorithm achieves an Overall Accuracy (OA) of 74.64% by using 9 input vectors and 14 shape features, whereas with the same 9 input vectors and only 5PCs (principal components built by the 14 shape features) it actually achieves a higher OA of 75.36% which demonstrates the effect of efficient dimensionality reduction.

Dimension Reduction
Feature Selection
LiDAR
Machine Learning
Neural Network
PCA
Semantic Segmentation

http://resolver.tudelft.nl/uuid:7c45e2df-7407-43fc-8d98-5184a0df731e

https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022

1682-1750

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 46 (2), 401-408

9th International Workshop on 3D Virtual Reconstruction and Visualization of Complex Architectures, 3D-ARCH 2022, 2022-03-02 → 2022-03-04, Mantua, Italy

Institutional Repository

journal article

© 2022 A. Nurunnabi, F. N. Teferle, D. F. Laefer, R.C. Lindenbergh, A. Hunegnaw