TAILIEUCHUNG - FUZZY CLUSTERING ALGORITHMS ON LANDSAT IMAGES FOR DETECTION OF WASTE AREAS: A COMPARISON

Remote sensing can be used to support a wide range of applications in Earth’s land surface information management. Typical applications concern, ., the mapping of changes due to the effects of pollution and environmental degradation over different periods of time, thanks to the high frequency of coverage of the Earth surface by satellites. | FUZZY CLUSTERING ALGORITHMS ON LANDSAT IMAGES FOR DETECTION OF WASTE AREAS: A COMPARISON . Massone(1) F. Masulli(1,3) A. Petrosino(2) (1) Istituto Nazionale per la Fisica della Materia Via Dodecaneso 33, 16146 Genova, Italy (2) Istituto Nazionale per la Fisica della Materia Via S. Allende, I-84081 Baronissi (Salerno), Italy (3) Dipartimento di Informatica e Scienze dell’Informazione Universit`adi Genova, Via Dodecaneso 35 16146 Genova, Italy Abstract - Landsat data can be used to support a wide range of applications for monitoring the conditions of a selected land surface. For example, they can be used to map changes due to the effects of pollution and environmental degradation over different periods of time. In this paper we will present a comparison of fuzzy clustering algorithms for the segmentation of multi-temporal Landsat images. A relabeling stage is performed after the classification in such a way clusters of different segmentations, but corresponding to the same lithological area, are led to a homogeneous color-map. Keywords: Fuzzy clustering algorithms, Landsat images segmentation, detection of waste. 1 Introduction Remote sensing can be used to support a wide range of applications in Earth’s land surface information management. Typical applications concern, ., the mapping of changes due to the effects of pollution and environmental degradation over different periods of time, thanks to the high frequency of coverage of the Earth surface by satellites. An important class of algorithms used in remote sensing image analysis, is constituted by unsupervised classification (or clustering) algorithms [4]. As pointed out by the recent literature (see, ., Baraldi et al. [1]) clustering algorithms can overcome the limits of classi- cal classifiers, such as the need of a priori hypothesis on the data distribution, sequentiality, etc. Moreover, the use of unsupervised algorithms is supported by the following arguments: Often clustering algorithms are faster and more

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