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EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD
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| Zeitschriftentitel: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
|---|---|
| Personen und Körperschaften: | , |
| In: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3, 2018, S. 2017-2022 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| Schlagwörter: |
| author_facet |
Xu, Z. Yang, Z. Xu, Z. Yang, Z. |
|---|---|
| author |
Xu, Z. Yang, Z. |
| spellingShingle |
Xu, Z. Yang, Z. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD General Earth and Planetary Sciences General Environmental Science |
| author_sort |
xu, z. |
| spelling |
Xu, Z. Yang, Z. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-3-2017-2018 <jats:p>Abstract. The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, a convolutional neural network based method achieves the best overall accuracy performance in all participants who only use the geometrical and waveform based features extracted from the ALS data. Features of the point are calculated in different scales to achieve the best performance. It is not efficiency for the future use. In this paper, we use an eigenentropy based scale selection strategy to improve this method. The scale selection strategy improves the average F1 score and makes the classification method more simple and efficient. </jats:p> EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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10.5194/isprs-archives-xlii-3-2017-2018 |
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Online Free |
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Copernicus GmbH, 2018 |
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Copernicus GmbH, 2018 |
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2194-9034 |
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2018 |
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Copernicus GmbH |
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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49 |
| title |
EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_unstemmed |
EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_full |
EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_fullStr |
EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_full_unstemmed |
EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_short |
EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_sort |
eigenentropy based convolutional neural network based als point clouds classification method |
| topic |
General Earth and Planetary Sciences General Environmental Science |
| url |
http://dx.doi.org/10.5194/isprs-archives-xlii-3-2017-2018 |
| publishDate |
2018 |
| physical |
2017-2022 |
| description |
<jats:p>Abstract. The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, a convolutional neural network based method achieves the best overall accuracy performance in all participants who only use the geometrical and waveform based features extracted from the ALS data. Features of the point are calculated in different scales to achieve the best performance. It is not efficiency for the future use. In this paper, we use an eigenentropy based scale selection strategy to improve this method. The scale selection strategy improves the average F1 score and makes the classification method more simple and efficient.
</jats:p> |
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| author | Xu, Z., Yang, Z. |
| author_facet | Xu, Z., Yang, Z., Xu, Z., Yang, Z. |
| author_sort | xu, z. |
| container_start_page | 2017 |
| container_title | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| container_volume | XLII-3 |
| description | <jats:p>Abstract. The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, a convolutional neural network based method achieves the best overall accuracy performance in all participants who only use the geometrical and waveform based features extracted from the ALS data. Features of the point are calculated in different scales to achieve the best performance. It is not efficiency for the future use. In this paper, we use an eigenentropy based scale selection strategy to improve this method. The scale selection strategy improves the average F1 score and makes the classification method more simple and efficient. </jats:p> |
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| imprint | Copernicus GmbH, 2018 |
| imprint_str_mv | Copernicus GmbH, 2018 |
| institution | DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1 |
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| physical | 2017-2022 |
| publishDate | 2018 |
| publishDateSort | 2018 |
| publisher | Copernicus GmbH |
| record_format | ai |
| recordtype | ai |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| source_id | 49 |
| spelling | Xu, Z. Yang, Z. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-3-2017-2018 <jats:p>Abstract. The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, a convolutional neural network based method achieves the best overall accuracy performance in all participants who only use the geometrical and waveform based features extracted from the ALS data. Features of the point are calculated in different scales to achieve the best performance. It is not efficiency for the future use. In this paper, we use an eigenentropy based scale selection strategy to improve this method. The scale selection strategy improves the average F1 score and makes the classification method more simple and efficient. </jats:p> EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Xu, Z., Yang, Z., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD, General Earth and Planetary Sciences, General Environmental Science |
| title | EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_full | EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_fullStr | EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_full_unstemmed | EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_short | EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| title_sort | eigenentropy based convolutional neural network based als point clouds classification method |
| title_unstemmed | EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD |
| topic | General Earth and Planetary Sciences, General Environmental Science |
| url | http://dx.doi.org/10.5194/isprs-archives-xlii-3-2017-2018 |