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Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area
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Author(s): Eufemia Tarantino (Politecnico di Bari, Italy), Antonio Novelli (Politecnico di Bari, Italy), Mariella Aquilino (Politecnico di Bari, Italy), Benedetto Figorito (ARPA Puglia, Italy)and Umberto Fratino (Politecnico di Bari, Italy)
Copyright: 2016
Pages: 18
Source title:
Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-9619-8.ch063
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Abstract
This chapter 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.
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