دورية أكاديمية
PAIRWISE-SVM FOR ON-BOARD URBAN ROAD LIDAR CLASSIFICATION
العنوان: | PAIRWISE-SVM FOR ON-BOARD URBAN ROAD LIDAR CLASSIFICATION |
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المؤلفون: | Z. Shu, K. Sun, K. Qiu, K. Ding |
المصدر: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLI-B1, Pp 109-113 (2016) |
بيانات النشر: | Copernicus Publications, 2016. |
سنة النشر: | 2016 |
المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Applied optics. Photonics |
مصطلحات موضوعية: | Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820 |
الوصف: | The common method of LiDAR classifications is Markov random fields (MRF). Based on construction of MRF energy function, spectral and directional features are extracted for on-board urban point clouds. The MRF energy function is consisted of unary and pairwise potentials. The unary terms are computed by SVM classifictaion. The initial labeling is mainly processed through geometrical shapes. The pairwise potential is estimated by Naïve Bayes. From training data, the probability of adjacent objects is computed by prior knowledge. The final labeling method is reweighted message-passing to minimization the energy function. The MRF model is difficult to process the large-scale misclassification. We propose a super-voxel clustering method for over-segment and grouping segment for large objects. Trees, poles ground, and building are classified in this paper. The experimental results show that this method improves the accuracy of classification and speed of computation. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 1682-1750 2194-9034 75954508 |
Relation: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/109/2016/isprs-archives-XLI-B1-109-2016.pdf; https://doaj.org/toc/1682-1750; https://doaj.org/toc/2194-9034 |
DOI: | 10.5194/isprs-archives-XLI-B1-109-2016 |
URL الوصول: | https://doaj.org/article/c759545086cf4fadb6ee781d14046c25 |
رقم الأكسشن: | edsdoj.759545086cf4fadb6ee781d14046c25 |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 16821750 21949034 75954508 |
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DOI: | 10.5194/isprs-archives-XLI-B1-109-2016 |