Abstract
Recently, image-filtering based hyperspectral image (HSI) feature extraction has been widely studied. However, due to limited spatial resolution and feature distribution complexity, the problems of cross-region mixing after filtering and spectral discriminative reduction still remain. To address these issues, this paper proposes a spectral-spatial propagation filter (PF) based HSI feature extraction method that can effectively address the above problems. The dimensionality/band of an HSI is typically high; therefore, principal component analysis (PCA) is first used to reduce the HSI dimensionality. Then, the principal components of the HSI are filtered with the PF. When cross-region mixture occurs in the image, the filter template reduces the weight assignments of the cross-region mixed pixels to handle the issue of cross-region mixed pixels simply and effectively. To validate the effectiveness of the proposed method, experiments are carried out on three common HSIs using support vector machine (SVM) classifiers with features learned by the PF. The experimental results demonstrate that the proposed method effectively extracts the spectral-spatial features of HSIs and significantly improves the accuracy of HSI classification.
Highlights
Hyperspectral images (HSIs) have many spectral bands and complex spatial structures that contain abundant information [1,2]
To validate the effectiveness of the proposed method, experiments are carried out on three common HSIs using support vector machine (SVM) classifiers with features learned by the propagation filter (PF)
The experimental results demonstrate that the proposed method effectively extracts the spectral-spatial features of HSIs and significantly improves the accuracy of HSI classification
Summary
Hyperspectral images (HSIs) have many spectral bands and complex spatial structures that contain abundant information [1,2]. PCA [23]; and the semi-supervised DR methods include semi-supervised discriminant analysis (SDA) [24] Among these methods, the new optimized feature extracted by the best discriminant vector satisfies the class separability after the samples in high-dimensional feature space are projected to the low-dimensional feature space through the supervised DR model LDA. Li et al [28] developed the PCA-Gabor-SVM algorithm, which improved HSI classification accuracy by combining spatial and spectral information to filter dimensionality-reduced features from PCA. The deep learning method proposed by Zhou et al [31] achieved very good results by using convolutional filters that learned directly from images by extracting their spectral-spatial features. This paper proposes a novel spectral-spatial feature extraction method of an HSI based on the PF method, which addresses the cross-regional mixing problem in HSIs effectively.
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