Minchao Ye

Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features

Hyperspectral remote sensing imagery contains rich information on spectral and spatial distributions of distinct surface materials. Owing to its numerous and continuous spectral bands, hyperspectral data enable more accurate and reliable material classification than using panchromatic or multispectral imagery. However, high-dimensional spectral features and limited number of available training samples have caused some difficulties in the classification, such as overfitting in learning, noise sensitiveness, overloaded computation, and lack of meaningful physical interpretability. In this paper, we propose a hyperspectral feature extraction and pixel classification method based on structured sparse logistic regression and 3-D discrete wavelet transform (3D-DWT) texture features. The 3D-DWT decomposes a hyperspectral data cube at different scales, frequencies, and orientations, during which the hyperspectral data cube is considered as a whole tensor instead of adapting the data to a vector or matrix. This allows the capture of geometrical and statistical spectral-spatial structures. After the feature extraction step, sparse representation/modeling is applied for data analysis and processing via sparse regularized optimization, which selects a small subset of the original feature variables to model the data for regression and classification purpose. A linear structured sparse logistic regression model is proposed to simultaneously select the discriminant features from the pool of 3D-DWT texture features and learn the coefficients of the linear classifier, in which the prior knowledge about feature structure can be mapped into the various sparsity-inducing norms such as lasso, group, and sparse group lasso. Furthermore, to overcome the limitation of linear models, we extended the linear sparse model to nonlinear classification by partitioning the feature space into subspaces of linearly separable samples. The advantages of our methods are validated on the real hyperspectral remote sensing data sets.

Hyperspectral Imagery Restoration Using Nonlocal Spectral-Spatial Structured Sparse Representation With Noise Estimation

Noise reduction is an active research area in image processing due to its importance in improving the quality of image for object detection and classification. In this paper, we develop a sparse representation based noise reduction method for hyperspectral imagery, which is dependent on the assumption that the non-noise component in an observed signal can be sparsely decomposed over a redundant dictionary while the noise component does not have this property. The main contribution of the paper is in the introduction of nonlocal similarity and spectral-spatial structure of hyperspectral imagery into sparse representation. Non-locality means the self-similarity of image, by which a whole image can be partitioned into some groups containing similar patches. The similar patches in each group are sparsely represented with a shared subset of atoms in a dictionary making true signal and noise more easily separated. Sparse representation with spectral-spatial structure can exploit spectral and spatial joint correlations of hyperspectral imagery by using 3-D blocks instead of 2-D patches for sparse coding, which also makes true signal and noise more distinguished. Moreover, hyperspectral imagery has both signal-independent and signal-dependent noises, so a mixed Poisson and Gaussian noise model is used. In order to make sparse representation be insensitive to the various noise distribution in different blocks, a variance-stabilizing transformation (VST) is used to make their variance comparable. The advantages of the proposed methods are validated on both synthetic and real hyperspectral remote sensing data sets.

Multitask Sparse Nonnegative Matrix Factorization for Joint Spectral–Spatial Hyperspectral Imagery Denoising

Hyperspectral imagery (HSI) denoising is a challenging problem because of the difficulty in preserving both spectral and spatial structures simultaneously. In recent years, sparse coding, among many methods dedicated to the problem, has attracted much attention and showed state-of-the-art performance. Due to the low-rank property of natural images, an assumption can be made that the latent clean signal is a linear combination of a minority of basis atoms in a dictionary, while the noise component is not. Based on this assumption, denoising can be explored as a sparse signal recovery task with the support of a dictionary. In this paper, we propose to solve the HSI denoising problem by sparse nonnegative matrix factorization (SNMF), which is an integrated model that combines parts-based dictionary learning and sparse coding. The noisy image is used as the training data to learn a dictionary, and sparse coding is used to recover the image based on this dictionary. Unlike most HSI denoising approaches, which treat each band image separately, we take the joint spectral-spatial structure of HSI into account. Inspired by multitask learning, a multitask SNMF (MTSNMF) method is developed, in which bandwise denoising is linked across the spectral domain by sharing a common coefficient matrix. The intrinsic image structures are treated differently but interdependently within the spatial and spectral domains, which allows the physical properties of the image in both spatial and spectral domains to be reflected in the denoising model. The experimental results show that MTSNMF has superior performance on both synthetic and real-world data compared with several other denoising methods.

3-D nonlocal means filter with noise estimation for hyperspectral imagery denoising

Noise reduction is one of important processing tasks for hyperspectral imagery (HSI). In this paper, a three-dimensional (3-D) nonlocal means filter is proposed for noise reduction of HSI. Recently, non-local means method attracts many attentions due to its global and local integrated property. Nonlocal algorithm searches the similar image patches in the whole scene to build the mean filter, so that it overcomes the disadvantage of local filter that only local pixels within a small neighbor is used, and the disadvantage of global filter that local structure is ignored. In order to explore the spectral-spatial correlation of HSI, nonlocal means method is extended from 2-D to 3-D. Furthermore, as HSI contains both of signal-independent and signal-dependent noises, variance-stabilizing transformation based on noise estimation is used to make noise reduction under the additive Gaussian noise model. Experiments with the real hyperspectral data set indicate that the proposed strategy can work well in both of detail preservation and noise removal.

Structured sparse model based feature selection and classification for hyperspectral imagery

Sparse modeling is a powerful framework for data analysis and processing. It is especially useful for high-dimensional regression and classification problems in which a large number of feature variables exist but the amount of training samples is limited. In this paper, we address the problems of feature description, feature selection and classifier design for hyperspectral images using structured sparse models. A linear sparse logistic regression model is proposed to combine feature selection and pixel classification into a regularized optimization problem with the constraint of sparsity. To explore the structured features, three-dimensional discrete wavelet transform (3D-DWT) is employed, which processes the hyperspectral data cube as a whole tensor instead of adapting the data to a vector or matrix. This allows more effective capturing of the spatial and spectral structure. The structure of the 3D-DWT features is imposed on the sparse model by group LASSO which selects the features on the group level. The advantages of our method are validated on the real hyperspectral data.

Noise reduction of hyperspectral imagery using nonlocal sparse representation with spectral-spatial structure

Noise reduction is always an active research area in image processing due to its importance for the sequential tasks such as object classification and detection. In this paper, we develop a sparse representation based noise reduction method for hyperspectral imagery, which is dependent on the assumption that the non-noise component in the signal can be approximated by only a small number of atoms in a dictionary while noise component has not this property. The main contribution of the paper is in introducing nonlocal similarity and spectral-spatial structure of hyperspectral imagery into sparse representation. Non-locality means the self-similarity of image, by which the whole image can be partitioned into some groups containing similar patches. The similar patches in each group is sparsely represented with shared atoms making the signal and noise more easily separated. Sparse representation with spectral-spatial structure can exploit spectral and spatial joint correlations of hyperspectral imagery also making the signal and noise more distinguished, in which 3-D blocks are instead of 2-D patches for sparse coding. The experimental results indicate that the proposed method has a good quality of restoring the true signal from the noisy observation.

Mixed Poisson-Gaussian noise model based sparse denoising for hyperspectral imagery

Sparse representation has been applied to image denoising in recent years. It is based on the assumption that the non-noise component in the signal can be approximated by only a small number of atoms in a dictionary while the noise component cannot. Previous researches have shown its excellent ability of noise reduction for images with signal-independent Gaussian noise. However, hyperspectral imagery has both of signal-independent and signal-dependent noise, so a mixed Poisson-Gaussian noise model is always used. In order to make sparse denoising method deal with such more complex noise model rather than just Gaussian noise model, the variance-stabilizing transformation (VST) and its inverse transformation are used before and after sparse denoising. The parameter estimation method for the mixed Poisson-Gaussian noise model is also discussed in this paper.

Noise reduction of hyperspectral imagery based on nonlocal tensor factorization

Noise reduction for hyperspectral imagery (HSI) is an indispensable step before further processes such as object detection and classification. In this paper, we propose a noise reduction method for HSI based on non-local strategy and tensor factorization. Based on the observation that natural images are always locally self-repetitive, we divide the whole HSI into small sub-blocks and cluster similar blocks into groups. Since similar blocks share the same underlying structure, the redundancy can be utilized to remove noise of the blocks jointly. We stack the similar blocks to construct a fourth-order tensor from each group. Noise is reduced by finding the lower dimensional approximation of each of the fourth-order tensors via Tucker factorization. The experimental results indicate that the proposed method has a good quality of restoring the true signal from the noisy observation.

Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification

A big challenge of hyperspectral image (HSI) classification is the small size of labeled pixels for training classifier. In real remote sensing applications, we always face the situation that an HSI scene is not labeled at all, or is with very limited number of labeled pixels, but we have sufficient labeled pixels in another HSI scene with the similar land cover classes. In this paper, we try to classify an HSI scene containing no labeled sample or only a few labeled samples with the help of a similar HSI scene having a relative large size of labeled samples. The former scene is defined as the target scene, while the latter one is the source scene. We name this classification problem as cross-scene classification. The main challenge of cross-scene classification is spectral shift, i.e., even for the same class in different scenes, their spectral distributions maybe have significant deviation. As all or most training samples are drawn from the source scene, while the prediction is performed in the target scene, the difference in spectral distribution would greatly deteriorate the classification performance. To solve this problem, we propose a dictionary learning-based feature-level domain adaptation technique, which aligns the spectral distributions between source and target scenes by projecting their spectral features into a shared low-dimensional embedding space by multitask dictionary learning. The basis atoms in the learned dictionary represent the common spectral components, which span a cross-scene feature space to minimize the effect of spectral shift. After the HSIs of two scenes are transformed into the shared space, any traditional HSI classification approach can be used. In this paper, sparse logistic regression (SRL) is selected as the classifier. Especially, if there are a few labeled pixels in the target domain, multitask SRL is used to further promote the classification performance. The experimental results on synthetic and real HSIs show the advantages of the proposed method for cross-scene classification.

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

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