Feature selection and classification of oil spills in SAR image based on statistics and artificial neural network

The general process of oil spill detection from SAR image with artificial neural network (ANN) classifier briefly includes five steps, target extraction, feature extraction, feature selection, ANN training and ANN classification. Feature extraction and feature selection are concerned in this paper....

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Bibliographic Details
Main Authors: Youjun Ma, Kan Zeng, Chaofang Zhao, Xintao Ding, Mingxia He
Format: Conference Proceeding
Language:English
Subjects:
ANN
Artificial neural networks
dark spot
Feature extraction
Image segmentation
Oceans
Oil spill
SAR
Synthetic aperture radar
Training
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Summary:The general process of oil spill detection from SAR image with artificial neural network (ANN) classifier briefly includes five steps, target extraction, feature extraction, feature selection, ANN training and ANN classification. Feature extraction and feature selection are concerned in this paper. Firstly, 68 features are calculated for each target. By cross-correlation analysis, 24 features are selected to build a neural network to classify oil spills and look-alikes. The impact of imbalance sample data set on the performance of classification is also considered. In the end, principal component analysis (PCA) is applied on 24 features to reduce the dimension of feature space. The best number of principal components is found out.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2014.6946486