In this article, we propose a new method for denoising hyperspectral imagery. Hyperspectral imagery normally contains a small amount of noise, which can hardly be seen by human eyes thanks to its relatively high signal-to-noise ratio. However, in many remote sensing applications, this amount of noise is still troublesome. In this study, we first perform principal component analysis (PCA) to the hyperspectral data cube to be denoised in order to separate the fine features from the noise in the hyperspectral data cube. Because the first few PCA output channels contain the majority of information in the hyperspectral data cube, we do not denoise these PCA output channel images. We use the block-matching 4D (BM4D) filtering to reduce the noise in the remaining low-energy noisy PCA output channel images. Finally, an inverse PCA transform is performed in order to obtain the denoised hyperspectral data cube. Experimental results show that our proposed method in this work is very competitive when compared with existing methods for hyperspectral imagery denoising. Dans cet article, nous proposons une nouvelle méthode pour le débruitage d’imagerie hyperspectrale. L’imagerie hyperspectrale contient normalement une petite quantité de bruit, qui peut difficilement être vu à l’œil nu à cause de son rapport signal sur bruit relativement élevé. Cependant, dans de nombreuses applications de télédétection, cette quantité de bruit est gênante. Dans cet article, nous réalisons d’abord une analyse en composantes principales (ACP; «PCA») sur le cube de données hyperspectrales qui sera débruité afin de séparer les traits fins du bruit dans ce cube de données hyperspectrales. Parce que les premières composantes de l’ACP contiennent la majorité de l’information du cube de données hyperspectrales, nous n’avons pas débruité ces sorties de l’ACP. Nous utilisons le filtrage 4D par mise en correspondance de blocs (« block-matching 4D filtering (BM4D) ») pour réduire le bruit dans les images de composantes principales restantes à basse énergie. Enfin, une transformation inverse de l’ACP est réalisée afin d’obtenir un cube de données hyperspectrales débruité. Les résultats expérimentaux montrent que la méthode proposée dans cet article est très compétitive par rapport aux méthodes existantes pour le débruitage de l’imagerie hyperspectrale.

In this paper, we study the Locally Linear Embedding (LLE) for nonlinear dimensionality reduction of hyperspectral data. We improve the existing LLE in terms of both computational complexity and memory consumption by introducing a spatial neighbourhood window for calculating the k nearest neighbours. The improved LLE can process larger hyperspectral images than the existing LLE and it is also faster. We conducted experiments of endmember extraction to assess the effectiveness of the dimensionality reduction methods. Experimental results show that the improved LLE is better than PCA and the existing LLE in identifying endmembers. It finds more endmembers than PCA and the existing LLE when the Pixel Purity Index (PPI) based endmember extraction method is used. Also, better results are obtained for detection.

Hyperspectral face recognition provides improved classification rates due to its abundant information in the face cubes of every subject in hyperspectral face databases. However, it is less popular in face recognition due to its difficulty in data acquisition, low signal‐to‐noise ratio, and high dimensionality. The authors compare five existing descriptors that are frequently used in 2D face recognition, and use collaborative representation classifier (CRC) with two voting techniques for hyperspectral face recognition. Experimental results demonstrate that, for PolyU‐HSFD database, Gabor filter bank‐based features are very robust to both Gaussian white noise and shot noise, and it achieves very competitive classification results. For CMU‐HSFD database, when the noise level is low, histogram of oriented gradients (HOG) yields good classification results. In addition, when the noise level is high, raw facial images without feature extraction perform very well in term of correct classification rate. The local binary pattern and HOG descriptor are very sensitive to noise even though they achieve rather good classification rates if the facial images contain no noise. The best recognition result for the PolyU‐HSFD is 96.4% ± 2.3 and for the CMU‐HSFD is 98.0% ± 0.7.

A new palmprint classification method is proposed in this paper by using the dual-tree complex wavelet transform. The dual-tree complex wavelet transform has such important properties as the approximate shift-invariance and high directional selectivity. These properties are very important in invariant palmprint classification. Support vector machines are used as a classifier and the Gaussian radial basis function kernel is selected in the experiments. Experimental results show that the dual-tree complex wavelet features outperform the scalar wavelet features, and three previously developed methods. We conclude that the dual-tree complex wavelet features should be used for invariant palmprint classification instead of the scalar wavelet features.

Even though the noise level in a hyperspectral data cube may be low, it still can affect most remote sensing applications. In this paper, a novel method is proposed for reducing noise in a hyperspectral data cube. The method first used principal component analysis (PCA) to decorrelate the useful information from noise and then applied block-matching and 3-D filtering to selectively reduce noise in the noisy PCA components. Finally, an inverse PCA was conducted to obtain a denoised data cube. The first few PCA components contained the majority of information in the hyperspectral data cube and very little noise. Because the method did not denoise the first few PCA components, most fine feature details in the data cube were retained after denoising. The signal-to-noise ratio after denoising the Greater Victoria Watershed District and the Cuprite data cubes improved significantly. From our experiments, the proposed method was very competitive when compared with other existing denoising methods published in th... Même si le niveau de bruit dans un cube de données hyperspectrales est plutôt faible, il peut encore affecter la plupart des applications en télédétection. Dans cet article, on propose une méthode novatrice pour réduire le bruit dans un cube de données hyperspectrales. La méthode consiste à utiliser au départ une analyse en composantes principales (ACP) pour décorréler l'information utile du bruit et ensuite à appliquer la méthode d'appariement par blocs et le filtrage 3D pour réduire sélectivement le bruit dans les composantes bruitées de l'ACP. Enfin, une ACP inverse est appliquée afin d'obtenir un cube de données débruitées. Les premières composantes de l'ACP contiennent la majorité de l'information du cube de données hyperspectrales et très peu de bruit. Comme la méthode ne débruite pas les premières composantes de l'ACP, la plupart des caractéristiques fines dans le cube de données seront retenues après le débruitage. Le rapport signal/bruit après le débruitage des cubes de données des sites du « Gre...