Details
| Original language | English |
|---|---|
| Pages (from-to) | 186-200 |
| Number of pages | 15 |
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
| Volume | 62 |
| Issue number | 3 |
| Early online date | 1 Jun 2007 |
| Publication status | Published - 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.
Keywords
- Fuzzy logic, Knowledge-base representation, Multitemporal interpretation, Remote sensing
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Engineering (miscellaneous)
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
Sustainable Development Goals
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In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 62, No. 3, 08.2007, p. 186-200.
Research output: Contribution to journal › Article › Research › 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 -