Fisher Vector Encoding of Supervoxel-Based Features for Airborne LiDAR Data Classification
| Autor: | Juntao Yang, Zhizhong Kang, Perpetual Hope Akwensi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Image fusion
business.industry Computer science Data classification Feature extraction 0211 other engineering and technologies Point cloud Fisher vector Pattern recognition 02 engineering and technology Geotechnical Engineering and Engineering Geology Photogrammetry Lidar Discriminative model Ranking Artificial intelligence Electrical and Electronic Engineering business 021101 geological & geomatics engineering |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters. 17:504-508 |
| ISSN: | 1558-0571 1545-598X |
| Popis: | Point cloud feature extraction as a classification task is crucial in maximizing the efficient downstream applicability of raw point clouds. With the goal of learning optimum features for efficient classification of a multi-class point cloud for downstream applications, this letter presents a supervoxel-Fisher vector (FV)-based approach for airborne light detection and ranging (LiDAR) data classification. In our approach, FV encoding is implemented to deduce compact global descriptors from aggregated supervoxels to establish a more descriptive and discriminative representation, transforming the low-level visual features into high-level semantic features. As a result, the proposed approach combines local and global feature properties through the quantization and aggregation of higher order statistics to harnesses their combined advantages for producing good classification results. Experiments were conducted on the international society for photogrammetry and remote sensing 3-D semantic labeling benchmark data set. Results indicate that the proposed approach is robust and efficient, attained the third best position with an overall accuracy of 81.79%, and ranked first with an $\text{F}_{{1}}$ -score of 72.31%. |
| Databáze: | OpenAIRE |
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