Details
| Original language | English |
|---|---|
| Publication status | Published - 2015 |
| Event | 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015 - Quezon City, Metro Manila, Philippines Duration: 24 Oct 2015 → 28 Oct 2015 |
Conference
| Conference | 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015 |
|---|---|
| Country/Territory | Philippines |
| City | Quezon City, Metro Manila |
| Period | 24 Oct 2015 → 28 Oct 2015 |
Abstract
This paper discusses random field based image classification methods, and in particular conditional random fields (CRF), for topographic mapping. A short review of the CRF principles reveals their main advantages, namely the possibility to incorporate local context into the classification to quantify the quality of the results in terms of probabilities. Three examples, the classification of point cloud data, multi-temporal and multi-scale classification of satellite images of different epochs and geometric resolution as well as the verification of existing land use data demonstrate the power and flexibility of CRF, but also its limitation in terms of capturing long range context. The paper closes with a short discussion on how to overcome this deficiency in the future.
Keywords
- Conditional random fields, Image classification, Topographic mapping
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
Sustainable Development Goals
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2015. Paper presented at 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015, Quezon City, Metro Manila, Philippines.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Image analysis based on probabilistic models
AU - Heipke, Christian
AU - Rottensteiner, Franz
PY - 2015
Y1 - 2015
N2 - This paper discusses random field based image classification methods, and in particular conditional random fields (CRF), for topographic mapping. A short review of the CRF principles reveals their main advantages, namely the possibility to incorporate local context into the classification to quantify the quality of the results in terms of probabilities. Three examples, the classification of point cloud data, multi-temporal and multi-scale classification of satellite images of different epochs and geometric resolution as well as the verification of existing land use data demonstrate the power and flexibility of CRF, but also its limitation in terms of capturing long range context. The paper closes with a short discussion on how to overcome this deficiency in the future.
AB - This paper discusses random field based image classification methods, and in particular conditional random fields (CRF), for topographic mapping. A short review of the CRF principles reveals their main advantages, namely the possibility to incorporate local context into the classification to quantify the quality of the results in terms of probabilities. Three examples, the classification of point cloud data, multi-temporal and multi-scale classification of satellite images of different epochs and geometric resolution as well as the verification of existing land use data demonstrate the power and flexibility of CRF, but also its limitation in terms of capturing long range context. The paper closes with a short discussion on how to overcome this deficiency in the future.
KW - Conditional random fields
KW - Image classification
KW - Topographic mapping
UR - http://www.scopus.com/inward/record.url?scp=84964057539&partnerID=8YFLogxK
M3 - Paper
T2 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015
Y2 - 24 October 2015 through 28 October 2015
ER -