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ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION
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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-2/W13, 2019, S. 1033-1037 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| Schlagwörter: |
| author_facet |
Li, N. Pfeifer, N. Li, N. Pfeifer, N. |
|---|---|
| author |
Li, N. Pfeifer, N. |
| spellingShingle |
Li, N. Pfeifer, N. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION General Earth and Planetary Sciences General Environmental Science |
| author_sort |
li, n. |
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Li, N. Pfeifer, N. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1033-2019 <jats:p>Abstract. Training dataset generation is a difficult and expensive task for LiDAR point classification, especially in the case of large area classification. We present a method to automatically extent a small set of training data by label propagation processing. The class labels could be correctly extended to their optimal neighbourhood, and the most informative points are selected and added into the training set. With the final extended training dataset, the overall (OA) classification could be increased by about 2%. We also show that this approach is stable regardless of the number of initial training points, and achieve better improvements especially stating with an extremely small initial training set. </jats:p> ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Copernicus GmbH, 2019 |
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Copernicus GmbH, 2019 |
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| title |
ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_unstemmed |
ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_full |
ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_fullStr |
ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_full_unstemmed |
ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_short |
ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_sort |
active learning to extend training data for large area airborne lidar classification |
| topic |
General Earth and Planetary Sciences General Environmental Science |
| url |
http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1033-2019 |
| publishDate |
2019 |
| physical |
1033-1037 |
| description |
<jats:p>Abstract. Training dataset generation is a difficult and expensive task for LiDAR point classification, especially in the case of large area classification. We present a method to automatically extent a small set of training data by label propagation processing. The class labels could be correctly extended to their optimal neighbourhood, and the most informative points are selected and added into the training set. With the final extended training dataset, the overall (OA) classification could be increased by about 2%. We also show that this approach is stable regardless of the number of initial training points, and achieve better improvements especially stating with an extremely small initial training set.
</jats:p> |
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| container_title | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| description | <jats:p>Abstract. Training dataset generation is a difficult and expensive task for LiDAR point classification, especially in the case of large area classification. We present a method to automatically extent a small set of training data by label propagation processing. The class labels could be correctly extended to their optimal neighbourhood, and the most informative points are selected and added into the training set. With the final extended training dataset, the overall (OA) classification could be increased by about 2%. We also show that this approach is stable regardless of the number of initial training points, and achieve better improvements especially stating with an extremely small initial training set. </jats:p> |
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| imprint | Copernicus GmbH, 2019 |
| imprint_str_mv | Copernicus GmbH, 2019 |
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| physical | 1033-1037 |
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| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| spelling | Li, N. Pfeifer, N. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1033-2019 <jats:p>Abstract. Training dataset generation is a difficult and expensive task for LiDAR point classification, especially in the case of large area classification. We present a method to automatically extent a small set of training data by label propagation processing. The class labels could be correctly extended to their optimal neighbourhood, and the most informative points are selected and added into the training set. With the final extended training dataset, the overall (OA) classification could be increased by about 2%. We also show that this approach is stable regardless of the number of initial training points, and achieve better improvements especially stating with an extremely small initial training set. </jats:p> ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Li, N., Pfeifer, N., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION, General Earth and Planetary Sciences, General Environmental Science |
| title | ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_full | ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_fullStr | ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_full_unstemmed | ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_short | ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| title_sort | active learning to extend training data for large area airborne lidar classification |
| title_unstemmed | ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION |
| topic | General Earth and Planetary Sciences, General Environmental Science |
| url | http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1033-2019 |