Cascade multitemporal classification based on fuzzy Markov chains |
| |
| Authors: | Raul Q Feitosa Gilson AOP Costa Guilherme LA Mota Kian Pakzad Maria CO Costa |
| |
| Institution: | 1. Catholic University of Rio de Janeiro, Department of Electrical Engineering, Rua Marquês de São Vicente 225, Room 401 L, Gávea, Rio de Janeiro, RJ 22453-900, Brazil;2. Rio de Janeiro State University, Department of Systems and Computer Engineering, Rua São Francisco Xavier 524, Room 5028 D, Maracanã, Rio de Janeiro, RJ 20550-900, Brazil;3. University of Hannover, Institute of Photogrammetry and GeoInformation, 1 Nienburger Street, Hannover D-30167, Germany;1. Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, via Donato Creti 12, I-40128 Bologna, Italy;2. Università di Padova, Dipartimento di Geoscienze, via Gradenigo 6, I-35131 Padova, Italy;3. Università di Bologna, Dipartimento di Architettura e Pianificazione Territoriale, viale Risorgimento 2, I-40136 Bologna, Italy;4. Università di Bologna, Dipartimento di Fisica, viale Pichat 8, I-40127 Bologna, Italy;1. Department of Geography, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada;2. Canadian Pacific Railway, 7550 Ogden Dale Road S.E., Calgary, AB T2C 4X9, Canada;3. Department of Physics & Astronomy, University of Alabama, Box 870324, Tuscaloosa, AL 35487-0324, USA;1. State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, PR China;2. School of Environment, Tsinghua University, Beijing 100084, PR China;3. Department of Geography, University of Utah, Salt Lake City, UT 84112, USA;1. Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Metaxa and Vas. Pavlou Str., GR-152 36 Penteli, Athens, Greece;2. University of Ioannina, Department of Biological Applications and Technology, Laboratory of Botany, GR-451 10 Ioannina, Greece;1. Beijing Research Center for Information Technology in Agriculture, Beijing, China;2. Beijing Academy of Agriculture and Forestry Sciences, Beijing, China;3. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, China;4. Department of Geography, Environment, and Planning, University of South Florida, USA;5. Chinese Academy of Surveying and Mapping, Beijing, China;1. Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, 1 Forestry Dr., Syracuse, NY 13210, USA;2. Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, 1 Forestry Dr., Syracuse, NY 13210, USA |
| |
| Abstract: | This paper proposes a new fuzzy cascade multitemporal classification method based on Fuzzy Markov Chains. This method differs from prior fuzzy multitemporal approaches proposed thus far, as the method does not require the knowledge of the true class at an earlier date; instead it uses the attributes of the image object being classified at the earlier date. This method combines the fuzzy, non-temporal, classification of a geographical region at two points in time to provide a single unified result. A transformation law based on class transition possibilities projects the earlier classification onto the later date before combining both results. Performance analysis was conducted upon a sequence of three LANDSAT images from the central region of Brazil using a Genetic Algorithm to estimate transition possibilities. The analysis showed that the increase in performance is highly dependent on whether or not a significant correlation exists between the temporal data sets, as well as on the accuracy of the monotemporal classifier at the earlier date. While the monotemporal approach used in the experiments attained an average class accuracy of approximately 55%, the multitemporal scheme achieved between 65% and 95%. Similar results in terms of overall accuracy were also observed. Furthermore, compared to two alternative cascade multitemporal classification approaches, the proposed method consistently showed better results. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
