Abstract
Earlier research has discussed the concept of discriminant space and its applications in area-class mapping and uncertainty characterization. Both simple univariate cases with b=1 (b being the dimension of the discriminant space) and multivariate cases with b>1 were analyzed with simulated and real data sets, respectively. This paper describes combined use of generalized linear models and kriging for scalable area-class mapping, with the former deterministically predicting mean class responses and the latter making use of spatially correlated residuals in the predictive class models. Scalability in area-class mapping is facilitated by scale-dependent prediction of mean class responses and kriging of the residuals over specific gridding cells. The methodology was implemented with topographic data and Landsat TM imagery concerning land cover mapping in central western Montana, which confirmed the effectiveness of the proposed strategy combining regression and kriging for scalable mapping of area classes.
| Original language | English (US) |
|---|---|
| Title of host publication | MIPPR 2007 |
| Subtitle of host publication | Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications |
| Volume | 6790 |
| DOIs | |
| State | Published - Dec 1 2007 |
| Externally published | Yes |
| Event | MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications - Wuhan, China Duration: Nov 15 2007 → Nov 17 2007 |
Other
| Other | MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications |
|---|---|
| Country | China |
| City | Wuhan |
| Period | 11/15/07 → 11/17/07 |
Fingerprint
Keywords
- Area-class maps
- Discriminant space
- Generalized linear models
- Geostatistics
- Scale
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
Discriminant models for scaling area-class maps. / Jingxiong, Zhang; Goodchild, Michael; Steele, Brian M.
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications. Vol. 6790 2007. 67903B.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Discriminant models for scaling area-class maps
AU - Jingxiong, Zhang
AU - Goodchild, Michael
AU - Steele, Brian M.
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Earlier research has discussed the concept of discriminant space and its applications in area-class mapping and uncertainty characterization. Both simple univariate cases with b=1 (b being the dimension of the discriminant space) and multivariate cases with b>1 were analyzed with simulated and real data sets, respectively. This paper describes combined use of generalized linear models and kriging for scalable area-class mapping, with the former deterministically predicting mean class responses and the latter making use of spatially correlated residuals in the predictive class models. Scalability in area-class mapping is facilitated by scale-dependent prediction of mean class responses and kriging of the residuals over specific gridding cells. The methodology was implemented with topographic data and Landsat TM imagery concerning land cover mapping in central western Montana, which confirmed the effectiveness of the proposed strategy combining regression and kriging for scalable mapping of area classes.
AB - Earlier research has discussed the concept of discriminant space and its applications in area-class mapping and uncertainty characterization. Both simple univariate cases with b=1 (b being the dimension of the discriminant space) and multivariate cases with b>1 were analyzed with simulated and real data sets, respectively. This paper describes combined use of generalized linear models and kriging for scalable area-class mapping, with the former deterministically predicting mean class responses and the latter making use of spatially correlated residuals in the predictive class models. Scalability in area-class mapping is facilitated by scale-dependent prediction of mean class responses and kriging of the residuals over specific gridding cells. The methodology was implemented with topographic data and Landsat TM imagery concerning land cover mapping in central western Montana, which confirmed the effectiveness of the proposed strategy combining regression and kriging for scalable mapping of area classes.
KW - Area-class maps
KW - Discriminant space
KW - Generalized linear models
KW - Geostatistics
KW - Scale
UR - http://www.scopus.com/inward/record.url?scp=42549125529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=42549125529&partnerID=8YFLogxK
U2 - 10.1117/12.751171
DO - 10.1117/12.751171
M3 - Conference contribution
SN - 9780819469540
VL - 6790
BT - MIPPR 2007
ER -