Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area

Eufemia Tarantino, Antonio Novelli, Mariella Aquilino, Benedetto Figorito and Umberto Fratino
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Eufemia Tarantino: Department of Civil, Environmental, Land, Building and Chemistry, Politecnico di Bari, Bari, Italy
Antonio Novelli: Department of Civil, Environmental, Land, Building and Chemistry, Politecnico di Bari, Bari, Italy
Mariella Aquilino: Department of Civil, Environmental, Land, Building and Chemistry, Politecnico di Bari, Bari, Italy
Benedetto Figorito: ARPA Puglia, Bari, Italy
Umberto Fratino: Department of Civil, Environmental, Land, Building and Chemistry, Politecnico di Bari, Bari, Italy

International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2015, vol. 6, issue 4, 83-102

Abstract: This paper analyzes two pixel-based classification approaches to support the analysis of land cover transformations based on multitemporal LANDSAT sensor data covering a time space of about 24 years. The research activity presented in this paper was carried out using Lama San Giorgio (Bari, Italy) catchment area as a study case, being this area prone to flooding as proved by its geological and hydrological characteristics and by the significant number of floods occurred in the past. Land cover classes were defined in accordance with on the CN method with the aim of characterizing land use based on attitude to generate runoff. Two different classifiers, i.e. Maximum Likelihood Classifier (MLC) and Java Neural Network Simulator (JavaNNS) models, were compared. The Artificial Neural Networks (ANN) approach was found to be the most reliable and efficient when lacking ground reference data and a priori knowledge on input data distribution.

Date: 2015
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