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https://hdl.handle.net/20.500.14279/1854| Title: | Classification of satellite images for land-cover changes using an unsupervised neural network algorithm | Authors: | Hadjimitsis, Diofantos G. Evangelou, I. Retalis, Adrianos Lazakidou, A. Clayton, Chris R I |
Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering | Keywords: | Neural networks;Remote-sensing images;Self-organizing maps | Issue Date: | Nov-2005 | Source: | WSEAS Transactions on Signal Processing, 2005, vol. 1, no. 2, pp. 155-162 | Volume: | 1 | Issue: | 2 | Start page: | 155 | End page: | 162 | Journal: | WSEAS Transactions on Signal Processing | Abstract: | This paper presents a method for classifying Landsat Satellite Images. This method is based on the Self-Organizing Map (SOM), which is an unsupervised artificial neural network algorithm. The occurrence of fires on the island of Skiathos in Greece is considered as an example for investigating the possible impact of fires on Land-Cover changes. Classification is performed on three Landsat-5 TM satellite images of areas of the island, acquired in 1988, 1999 and 2000. Land-cover changes and areas affected by the fires are identified after the classification has been performed on the images. In order to assess the proposed SOM Artificial Neural network algorithm, the same images have been classified using conventional classification methods and their results are compared with the ones of the SOM. | URI: | https://hdl.handle.net/20.500.14279/1854 | ISSN: | 22243488 | Rights: | © Wseas | Type: | Article | Affiliation: | Frederick Institute of Technology | Affiliation : | Frederick Institute of Technology | Publication Type: | Peer Reviewed |
| Appears in Collections: | Άρθρα/Articles |
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