Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 186-200 |
| Seitenumfang | 15 |
| Fachzeitschrift | ISPRS Journal of Photogrammetry and Remote Sensing |
| Jahrgang | 62 |
| Ausgabenummer | 3 |
| Frühes Online-Datum | 1 Juni 2007 |
| Publikationsstatus | Veröffentlicht - Aug. 2007 |
Abstract
This paper proposes a new method to model temporal knowledge and to combine it with spectral and spatial knowledge within an integrated fuzzy automatic image classification framework for land-use land-cover map update applications. The classification model explores not only the object features, but also information about its class at a previous date. The method expresses temporal class dependencies by means of a transition diagram, assigning a possibility value to each class transition. A Genetic Algorithm (GA) carries out the class transition possibilities estimation. Temporal and spectral/spatial classification results are combined by means of fuzzy aggregation. The improvement achieved by the use of multitemporal knowledge rather than a pure monotemporal approach was assessed in a real application using LANDSAT images from Midwest Brazil. The experiments showed that the use of temporal knowledge markedly improved the classification performance, in comparison to a conventional single-time classification. A further observation was that multitemporal knowledge may subsume the knowledge related to steady spatial attributes whose values do not significantly change over time.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Atom- und Molekularphysik sowie Optik
- Ingenieurwesen (insg.)
- Ingenieurwesen (sonstige)
- Informatik (insg.)
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
Ziele für nachhaltige Entwicklung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: ISPRS Journal of Photogrammetry and Remote Sensing, Jahrgang 62, Nr. 3, 08.2007, S. 186-200.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Multitemporal fuzzy classification model based on class transition possibilities
AU - Mota, Guilherme L.A.
AU - Feitosa, Raul Q.
AU - Coutinho, Heitor L.C.
AU - Liedtke, Claus Eberhard
AU - Müller, Sönke
AU - Pakzad, Kian
AU - Meirelles, Margareth S.P.
PY - 2007/8
Y1 - 2007/8
N2 - This paper proposes a new method to model temporal knowledge and to combine it with spectral and spatial knowledge within an integrated fuzzy automatic image classification framework for land-use land-cover map update applications. The classification model explores not only the object features, but also information about its class at a previous date. The method expresses temporal class dependencies by means of a transition diagram, assigning a possibility value to each class transition. A Genetic Algorithm (GA) carries out the class transition possibilities estimation. Temporal and spectral/spatial classification results are combined by means of fuzzy aggregation. The improvement achieved by the use of multitemporal knowledge rather than a pure monotemporal approach was assessed in a real application using LANDSAT images from Midwest Brazil. The experiments showed that the use of temporal knowledge markedly improved the classification performance, in comparison to a conventional single-time classification. A further observation was that multitemporal knowledge may subsume the knowledge related to steady spatial attributes whose values do not significantly change over time.
AB - This paper proposes a new method to model temporal knowledge and to combine it with spectral and spatial knowledge within an integrated fuzzy automatic image classification framework for land-use land-cover map update applications. The classification model explores not only the object features, but also information about its class at a previous date. The method expresses temporal class dependencies by means of a transition diagram, assigning a possibility value to each class transition. A Genetic Algorithm (GA) carries out the class transition possibilities estimation. Temporal and spectral/spatial classification results are combined by means of fuzzy aggregation. The improvement achieved by the use of multitemporal knowledge rather than a pure monotemporal approach was assessed in a real application using LANDSAT images from Midwest Brazil. The experiments showed that the use of temporal knowledge markedly improved the classification performance, in comparison to a conventional single-time classification. A further observation was that multitemporal knowledge may subsume the knowledge related to steady spatial attributes whose values do not significantly change over time.
KW - Fuzzy logic
KW - Knowledge-base representation
KW - Multitemporal interpretation
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=34447299159&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2007.04.001
DO - 10.1016/j.isprsjprs.2007.04.001
M3 - Article
AN - SCOPUS:34447299159
VL - 62
SP - 186
EP - 200
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
IS - 3
ER -