Abstract
Hyperspectral imagery contains both spectral information and spatial relationships among pixels. How to combine spatial information with spectral information effectively has always been a research hotspot of hyperspectral image classification. In this paper, a Spatial-Spectral Kernel Principal Component Analysis Network (SS-KPCANet) was proposed. The network is developed from the original structure of Principal Component Analysis Network. In which PCA is replaced by KPCA to extract more nonlinear features. In addition, the combination of spatial and spectral features also improves the performance of the network. At the end of the network, neighbourhood correction is added to further improve the classification accuracy. Experiments on three datasets show the effectiveness of the proposed method. Comparison with state-of-the-art deep learning-based methods indicate that the proposed method needs less training samples and has better performance.
Highlights
Hyperspectral remote sensing started from multispectral remote sensing in the 1980s, with the rapid development of information processing technology, hyperspectral has gradually become a hot research field of remote sensing
We proposed a Spatial-Spectral Kernel Principal Component Analysis Network (SS-KPCANet) for hyperspectral image classification inspired by principal component analysis network (PCANet)
Comparison with traditional machine learning algorithms Support vector machine (SVM)(RBF) shows that the SS-KPCANet reduces the occurrence of misclassification and makes the same region more uniform, overall accuracies (OA) increased by 12.99%, 15.12% and 7.62% on three datasets
Summary
Hyperspectral remote sensing started from multispectral remote sensing in the 1980s, with the rapid development of information processing technology, hyperspectral has gradually become a hot research field of remote sensing. The essence of PCANet is a simplified CNN model, in which two-level cascaded principal component analysis is used to replace the convolutional filter in CNN. Such a simple PCANet is on par with some state of art methods on the tasks of image classification [13,14]. We proposed a Spatial-Spectral Kernel Principal Component Analysis Network (SS-KPCANet) for hyperspectral image classification inspired by PCANet. Hyperspectral image contains certain nonlinear information while PCANet is mainly linear operation, considering the extraction of nonlinear feature may enhance the performance of the network, we improve the original structure of PCANet by the introduction of kernel function.
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