Details
| Original language | English |
|---|---|
| Pages (from-to) | 495-502 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 43 |
| Issue number | B2-2021 |
| Publication status | Published - 28 Jun 2021 |
| Event | 2021 24th ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow - Virtual, Online, France Duration: 5 Jul 2021 → 9 Jul 2021 |
Abstract
Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score.
Keywords
- Classification, CNN, Hierarchy, Land use database, Multi-scale
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
Sustainable Development Goals
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 43, No. B2-2021, 28.06.2021, p. 495-502.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - CNN-based multi-scale hierarchical land use classification for the verification of geospatial databases
AU - Yang, C.
AU - Rottensteiner, F.
AU - Heipke, C.
N1 - Funding Information: We thank the Landesamt für Geoinformation und Landesvermessung Niedersachsen (LGLN), the Landesamt für Vermessung und Geoinformation Schleswig-Holstein (LVermGeo) and Landesamt für innere Verwaltung Mecklenburg-Vorpommern (LaiV-MV) for providing the test data and for their support of this project.
PY - 2021/6/28
Y1 - 2021/6/28
N2 - Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score.
AB - Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score.
KW - Classification
KW - CNN
KW - Hierarchy
KW - Land use database
KW - Multi-scale
UR - http://www.scopus.com/inward/record.url?scp=85116022968&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLIII-B2-2021-495-2021
DO - 10.5194/isprs-archives-XLIII-B2-2021-495-2021
M3 - Conference article
AN - SCOPUS:85116022968
VL - 43
SP - 495
EP - 502
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - B2-2021
T2 - 2021 24th ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow
Y2 - 5 July 2021 through 9 July 2021
ER -