Details
| Original language | English |
|---|---|
| Article number | 6841049 |
| Pages (from-to) | 659-673 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Geoscience and Remote Sensing |
| Volume | 53 |
| Issue number | 2 |
| Publication status | Published - Feb 2015 |
Abstract
In this paper, we present a method for the multitemporal and contextual classification of georeferenced optical remote sensing images acquired at different epochs and having different geometrical resolutions. The method is based on Conditional Random Fields (CRFs) for contextual classification. The CRF model is expanded by temporal interaction terms that link neighboring epochs via transition probabilities between different classes. In order to be able to deal with data of different resolution, the class structure at different epochs may vary with the resolution. The goal of the multitemporal classification is an improved classification performance at all individual epochs, but also the detection of land-cover changes, possibly using lower resolution data. This paper also contains a comparison of the performance of different models for the interaction potentials. Results are given for two different test sites in Germany, where Ikonos, RapidEye, and Landsat images are available. Our results show that the multitemporal classification does indeed increase the overall accuracy of all epochs compared to a monotemporal classification and to a state-of-the-art multitemporal classification method, and that it is feasible to detect changes in lower resolution images.
Keywords
- Change detection, conditional random field (CRF), Markov random field (MRF), multiscale, multitemporal classification
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)
- General Earth and Planetary Sciences
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In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 2, 6841049, 02.2015, p. 659-673.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Conditional random fields for multitemporal and multiscale classification of optical satellite imagery
AU - Hoberg, Thorsten
AU - Rottensteiner, Franz
AU - Feitosa, Raul Queiroz
AU - Heipke, Christian
PY - 2015/2
Y1 - 2015/2
N2 - In this paper, we present a method for the multitemporal and contextual classification of georeferenced optical remote sensing images acquired at different epochs and having different geometrical resolutions. The method is based on Conditional Random Fields (CRFs) for contextual classification. The CRF model is expanded by temporal interaction terms that link neighboring epochs via transition probabilities between different classes. In order to be able to deal with data of different resolution, the class structure at different epochs may vary with the resolution. The goal of the multitemporal classification is an improved classification performance at all individual epochs, but also the detection of land-cover changes, possibly using lower resolution data. This paper also contains a comparison of the performance of different models for the interaction potentials. Results are given for two different test sites in Germany, where Ikonos, RapidEye, and Landsat images are available. Our results show that the multitemporal classification does indeed increase the overall accuracy of all epochs compared to a monotemporal classification and to a state-of-the-art multitemporal classification method, and that it is feasible to detect changes in lower resolution images.
AB - In this paper, we present a method for the multitemporal and contextual classification of georeferenced optical remote sensing images acquired at different epochs and having different geometrical resolutions. The method is based on Conditional Random Fields (CRFs) for contextual classification. The CRF model is expanded by temporal interaction terms that link neighboring epochs via transition probabilities between different classes. In order to be able to deal with data of different resolution, the class structure at different epochs may vary with the resolution. The goal of the multitemporal classification is an improved classification performance at all individual epochs, but also the detection of land-cover changes, possibly using lower resolution data. This paper also contains a comparison of the performance of different models for the interaction potentials. Results are given for two different test sites in Germany, where Ikonos, RapidEye, and Landsat images are available. Our results show that the multitemporal classification does indeed increase the overall accuracy of all epochs compared to a monotemporal classification and to a state-of-the-art multitemporal classification method, and that it is feasible to detect changes in lower resolution images.
KW - Change detection
KW - conditional random field (CRF)
KW - Markov random field (MRF)
KW - multiscale
KW - multitemporal classification
UR - http://www.scopus.com/inward/record.url?scp=84906326713&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2014.2326886
DO - 10.1109/TGRS.2014.2326886
M3 - Article
AN - SCOPUS:84906326713
VL - 53
SP - 659
EP - 673
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
IS - 2
M1 - 6841049
ER -