Automatic Dust Storm Detection Based on Supervised Classification of Multispectral Data

Autor:	Pablo Rivas-Perea, Mario Ignacio Chacon Murguia, James C. Tilton, Jose-Gerardo Rosiles
Rok vydání:	2010
Předmět:	Probabilistic neural network Dust storm Computer science Feature vector Multispectral image Data mining Spectral bands computer.software_genre computer Classifier (UML) Multispectral pattern recognition Remote sensing
Zdroj:	Soft Computing for Recognition Based on Biometrics ISBN: 9783642151101 Soft Computing for Recognition Based on Biometrics
DOI:	10.1007/978-3-642-15111-8_27
Popis:	This paper address the detection of dust storms based on a probabilistic analysis of multispectral images. We develop a feature set based on the analysis of spectral bands reported in the literature. These studies have focused on the visual identification of the image channels that reflect the presence of dust storms through correlation with meteorological reports. Using this feature set we develop a Maximum Likelihood classifier and a Probabilistic Neural Network (PNN) to automate the dust storm detection process. The data sets are MODIS multispectral bands from NASA Terra satellite. Findings indicate that the PNN provides improved classification performance with reference to the ML classifier. Furthermore, the proposed schemes allow real-time processing of satellite data at 1 km resolutions which is an improvement compared to the 10 km resolution currently provided by other detection methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d485d4121e5cc57db954ffe39146dfd1 https://doi.org/10.1007/978-3-642-15111-8_27 Zobrazit plný text záznamu