Journal ArticleDOI
PCA-Based Edge-Preserving Features for Hyperspectral Image Classification
TLDR
The proposed PCA-EPFs method for HSI classification sharply improves the accuracy of the SVM classifier with respect to the standard edge-preserving filtering-based feature extraction method, and other widely used spectral-spatial classifiers.Abstract:
Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to hyperspectral images (HSIs) have been found very effective in characterizing significant spectral and spatial structures of objects in a scene. However, a direct use of the EPFs can be insufficient to provide a complete characterization of spatial information when objects of different scales are present in the considered images. Furthermore, the edge-preserving smoothing operation unavoidably decreases the spectral differences among objects of different classes, which may affect the following classification. To overcome these problems, in this paper, a novel principal component analysis (PCA)-based EPFs (PCA-EPFs) method for HSI classification is proposed, which consists of the following steps. First, the standard EPFs are constructed by applying edge-preserving filters with different parameter settings to the considered image, and the resulting EPFs are stacked together. Next, the spectral dimension of the stacked EPFs is reduced with the PCA, which not only can represent the EPFs in the mean square sense but also highlight the separability of pixels in the EPFs. Finally, the resulting PCA-EPFs are classified by a support vector machine (SVM) classifier. Experiments performed on several real hyperspectral data sets show the effectiveness of the proposed PCA-EPFs, which sharply improves the accuracy of the SVM classifier with respect to the standard edge-preserving filtering-based feature extraction method, and other widely used spectral-spatial classifiers.read more
Citations
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Journal ArticleDOI
Classification of Hyperspectral Images by Gabor Filtering Based Deep Network
TL;DR: Experiments performed on four real hyperspectral datasets show that the proposed method outperforms several recently proposed classification methods in terms of classification accuracies.
Journal ArticleDOI
Classification of Hyperspectral Images Based on Multiclass Spatial–Spectral Generative Adversarial Networks
TL;DR: A novel multiclass spatial–spectral GAN (MSGAN) method is proposed that achieves encouraging classification performance compared with several state-of-the-art methods, especially with the limited training samples.
Journal ArticleDOI
Beyond the Patchwise Classification: Spectral-Spatial Fully Convolutional Networks for Hyperspectral Image Classification
Yonghao Xu,Bo Du,Liangpei Zhang +2 more
TL;DR: A novel mask matrix is proposed to assist the back-propagation in the training stage of HSI classification with an end-to-end, pixel- to-pixel architecture and the dense conditional random field is introduced into the framework to further balance the local and global information.
Journal ArticleDOI
PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification
TL;DR: The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands to improve the classification accuracy of these images.
Journal ArticleDOI
Unsupervised Feature Extraction in Hyperspectral Images Based on Wasserstein Generative Adversarial Network
TL;DR: A novel modified generative adversarial network (GAN) is proposed to train a DL-based feature extractor without supervision, and replaces the original Jensen–Shannon divergence with the Wasserstein distance, aiming to mitigate the unstability and difficulty of the training of GAN frameworks.
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