2026-06-08T09:50:59Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/513232024-11-27T15:03:04Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Del Amo, Ana
author
Gómez González, Daniel
author
Montero De Juan, Francisco Javier
2023-06-20T10:50:36Z
2023-06-20T10:50:36Z
2008
1134-5632
https://hdl.handle.net/20.500.14352/51323
http://ic.ugr.es/Mathware/index.php/Mathware/article/viewFile/45/28
The goal of this paper is to present an algorithm for pattern recognition,leveraging on an existing fuzzy clustering algorithm developed by Del Amo et al. [3, 5], and modifying it to its supervised version, in order to apply the algorithm to different pattern recognition applications in Remote Sensing.The main goal is to recognize the object and stop the search depending on the precision of the application. The referred algorithm was the core of a classification system based on Fuzzy Sets Theory (see [14]), approaching remotely sensed classification problems as multicriteria decision making problems, solved by means of an outranking methodology (see [12] and also [11]). The referred algorithm was a unsupervised classification algorithm, but now in this paper will present a modification of the original algorithm into a supervised version.
eng
Spectral Fuzzy Classification: A Supervised Approach
journal article
URL
https://docta.ucm.es/bitstreams/0a3772b6-18d5-49ca-be95-21de43fd3e83/download
File
MD5
db18d7420496edf6002da04838a6591f
144307
application/pdf
Montero115.pdf
URL
https://docta.ucm.es/bitstreams/f35686e0-27f0-4fc0-8c34-794be57e6541/download
File
MD5
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36645
text/plain
Montero115.pdf.txt