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Neural Network Approach for Automatic Landuse Classification of Satellite Images: One-Against-Rest and Multi-Class Classifiers
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 134 - Number 11 |
| Year of Publication: 2016 |
| Authors: Anil Kumar Goswami, Heena Joshi, S. P. Mishra |
10.5120/ijca2016908148
|
Anil Kumar Goswami, Heena Joshi, S. P. Mishra . Neural Network Approach for Automatic Landuse Classification of Satellite Images: One-Against-Rest and Multi-Class Classifiers. International Journal of Computer Applications. 134, 11 ( January 2016), 35-42. DOI=10.5120/ijca2016908148
Abstract
Artificial Neural Network (ANN) is an important Artificial Intelligence (AI) and Machine Learning (ML) method used in various remote sensing applications such as image classification, pattern recognition etc.One of important remote sensing applications is the landuse classification i.e. classification of satellite data into various landuse classes such as forest, waterbody, snowcover etc. Landuse classification from satellite data can take place in manual, semi-automatic or automatic mode. Automatic landuse classification is necessary to reduce manual efforts,which can be achieved by making use of machine learning techniques. This paper uses neural network approach for automatic landuse classification from satellite data by providing two classification approaches using multi layer perceptron (MLP) namely one against rest classification (OARC) and multi class classification (MCC), and then provides the comparison between these two approaches.
References
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