Segmentation of LiDAR Intensity Using CNN Feature Based on Weighted Voting
| Autor: | Masaki Umemura, Kazuhiro Hotta, Kazuo Oda, Hideki Nonaka |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Ground truth
010504 meteorology & atmospheric sciences Computer science business.industry media_common.quotation_subject Weighted voting Pattern recognition 02 engineering and technology 01 natural sciences Convolutional neural network Image (mathematics) Lidar Voting 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence business 0105 earth and related environmental sciences Mobile mapping media_common |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319598758 ICIAR |
| Popis: | We propose an image labeling method for LiDAR intensity image obtained by Mobile Mapping System (MMS). Conventional segmentation method using CNN and KNN could give high accuracy but the accuracies of objects with small area are much lower than other classes with large area. We solve this issue by using voting cost. The first cost is determined from a local region. Another cost is determined from surrounding regions of the local region. Those costs become large when labeling result corresponds to class label of the region. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images) and test sets (8 images). We use class average accuracy as evaluation measures. Our proposed method gain 84.75% on class average accuracy, and it is 9.22% higher than our conventional method. We demonstrated that the proposed costs are effective to improve the accuracy. |
| Databáze: | OpenAIRE |
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