Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

Abstract

Marine oil spills accidents has caused a large amount of crude oil to leak into the marine environment and threaten marine ecological environment. Hyperspectral remote sensing images (HRSI) technology can quickly and accurately identify oil film of different thickness on marine surface. In order to overcome the traditional spectrum analysis method and space extraction method of long time sampling, calculation, analysis and other shortcomings. On account of the advantages of the spectral and spatial information in the field of HRSI classification, a spectral-spatial features extraction (SSFE) method based convolutional neural networks (CNNs) was proposed to analyse oil spills. In this way, one and two dimensional models based on convolutional neural networks (1D-CNN,2D-CNN) have been introduced as the spectral and spatial features extractor. When extracting spatial features, double-two convolution layers are connected to increasing nonlinearity and reduce the number of parameters. Furthermore, in order to address overfitting and imbalance samples, L2 regularization, class_weight and drouput is added to classes data modeling. More importantly, principal component analysis (PCA) is applied to data dimension reduction, 1D-CNN and 2D-CNN is combined into a unified model further extract the joint spatial-spectral features. To evaluate the effectiveness of the proposed approach, three hyperspectral datasets were utilized, including: University of Pavia dataset, oil spill area 1, oil spill area 2. Experimental results reveal that the proposed method have a very satisfactory performance and better distinguish oil spills.