Abstract
Different views of spatial resolution and accuracy present a major obstacle to the integration of remote sensing and GIS. Accuracy in remote sensing is modeled using probabilities of class membership in each pixel; in vector-based GIS it is modeled using concepts such as the epsilon band. The problem of linking the two views of accuracy reduces to one of realizing a stochastic process which must satisfy conditions of prior and posterior probabilities, and spatial dependence. We propose two suitable methods, one storage intensive and the other computationally intensive. The methods can be adapted to incorporate various forms of prior knowledge. -Authors
| Original language | English (US) |
|---|---|
| Pages (from-to) | 530-537 |
| Number of pages | 8 |
| Journal | Unknown Journal |
| State | Published - Jan 1 1989 |
| Externally published | Yes |
Fingerprint
ASJC Scopus subject areas
- Environmental Science(all)
- Earth and Planetary Sciences(all)
Cite this
Modeling errors for remotely sensed data input to GIS. / Goodchild, Michael; Wang Min-Hua, Min-Hua.
In: Unknown Journal, 01.01.1989, p. 530-537.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Modeling errors for remotely sensed data input to GIS
AU - Goodchild, Michael
AU - Wang Min-Hua, Min-Hua
PY - 1989/1/1
Y1 - 1989/1/1
N2 - Different views of spatial resolution and accuracy present a major obstacle to the integration of remote sensing and GIS. Accuracy in remote sensing is modeled using probabilities of class membership in each pixel; in vector-based GIS it is modeled using concepts such as the epsilon band. The problem of linking the two views of accuracy reduces to one of realizing a stochastic process which must satisfy conditions of prior and posterior probabilities, and spatial dependence. We propose two suitable methods, one storage intensive and the other computationally intensive. The methods can be adapted to incorporate various forms of prior knowledge. -Authors
AB - Different views of spatial resolution and accuracy present a major obstacle to the integration of remote sensing and GIS. Accuracy in remote sensing is modeled using probabilities of class membership in each pixel; in vector-based GIS it is modeled using concepts such as the epsilon band. The problem of linking the two views of accuracy reduces to one of realizing a stochastic process which must satisfy conditions of prior and posterior probabilities, and spatial dependence. We propose two suitable methods, one storage intensive and the other computationally intensive. The methods can be adapted to incorporate various forms of prior knowledge. -Authors
UR - http://www.scopus.com/inward/record.url?scp=0024836980&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0024836980&partnerID=8YFLogxK
M3 - Article
SP - 530
EP - 537
JO - Scanning Electron Microscopy
JF - Scanning Electron Microscopy
SN - 0586-5581
ER -