Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 133-140 |
| Seitenumfang | 8 |
| Fachzeitschrift | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | 3 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 7 Juni 2016 |
| Veranstaltung | 23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016 - Prague, Tschechische Republik Dauer: 12 Juli 2016 → 19 Juli 2016 |
Abstract
Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
- Umweltwissenschaften (insg.)
- Umweltwissenschaften (sonstige)
- Physik und Astronomie (insg.)
- Instrumentierung
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in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang 3, Nr. 3, 07.06.2016, S. 133-140.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - USING LABEL NOISE ROBUST LOGISTIC REGRESSION FOR AUTOMATED UPDATING OF TOPOGRAPHIC GEOSPATIAL DATABASES
AU - Maas, A.
AU - Rottensteiner, F.
AU - Heipke, C.
N1 - Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2016/6/7
Y1 - 2016/6/7
N2 - Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.
AB - Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.
KW - Change detection
KW - label noise
KW - logistic regression
KW - supervised classification
UR - http://www.scopus.com/inward/record.url?scp=85013961283&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-III-7-133-2016
DO - 10.5194/isprs-annals-III-7-133-2016
M3 - Conference article
AN - SCOPUS:85013961283
VL - 3
SP - 133
EP - 140
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 3
T2 - 23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016
Y2 - 12 July 2016 through 19 July 2016
ER -