Haoliang Yuan

• University of Macau

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the impo...

It is known that hyperspectral image (HSI) classification is a high-dimension low-sample-size problem. To ease this problem, one natural idea is to take the feature extraction as a preprocessing. A graph embedding model is a classic family of feature extraction methods, which preserves certain statistical or geometric properties of the data set. Ho...

Sparse representation-based classification model has been widely applied into hyperspectral image (HSI) classification. Its mechanism is based on the assumption that the nonzero coefficients in the sparse representation mainly lie in the correct class-dependent low-dimensional subspace. However, the high similarity of pixels between some different...

Time series forecasting is a widely and important research area in signal processing and machine learning. With the development of the artificial intelligence (AI), more and more AI technologies are used in time series forecasting. Multi-layer network structure has been widely used for forecasting problems. In this paper, based on a data-driven and...

This paper propose a least square-based sparse representation algorithm to analyze similarity comparison of protein sequences in the area of bioinformatics and molecular biology, which helps the prediction and classification of protein structure and function. The protein sequences are represented into the 1-dimensional feature vectors by their bioc...

Signal processing on graphs is a new emerging field that processing high-dimensional data by spreading samples on networks or graphs. The new introduced definition of graph Fourier transform shows its importance in establishing the theory of frequency analysis or computational harmonic analysis on graph signal processing. We introduce the definitio...

Recent research has shown that utilizing the spectral-spatial information can improve the performance of hyperspectral image (HSI) classification. Since HSI is a 3-D cube datum, 3-D spatial filtering becomes a simple and effective method for extracting the spectral-spatial information. In this paper, we propose a 3-D scattering wavelet transform, w...

Sparsity-based model has been successfully applied in hyper spectral image classification. However, previous l1-based method fails to consider the spatial structure of each pixel. In this paper, we generalize the l1-based method to its tensor form, which takes full advantage of the spatial structure of the pixel. To optimize the scale of the spatia...

A sparsity-based model has led to interesting results in hyperspectral image (HSI) classification. Sparse representation from a test sample is used to identify the class label. However, an $ell_{1}$-based sparse algorithm sometimes yields unstable sparse representation. Inspired by recent progress in manifold learning, two manifold-based sparse rep...

It is a significant issue to find similar proteins from a large scale of protein database efficiently. This paper presents a new algorithm of protein sequence which is based on fractal dimension and wavelet transform. A hybrid method consisting fractal dimension calculation, discrete wavelet transform and sliding window are applied to generate a ne...

Various sparsity-based methods have been widely used in hyperspectral image (HSI) classification. To determine the class label of a test sample, traditional sparsity-based frameworks mainly use the sparse vectors to compute the residual error for classification. In this paper, a novel sparsity-based framework is proposed, which adopts the max pooli...

This paper proposes a spectral-spatial linear discriminant analysis (LDA) method for the hyperspectral image classification. A natural assumption is that similar samples have similar structure in the dimensionality reduced feature space. The proposed method uses a local scatter matrix from a small neighborhood as a regularizer incorporated into the...

Sparsity-based models have been widely applied to hyperspectral image (HSI) classification. The class label of the test sample is determined by the minimum residual error based on the sparse vector, which is viewed as a pattern of original sample in the sparsity-based model. From the aspect of pattern classification, similar samples in the same cla...

The Compressive-Projection Principle Component Analysis (CPPCA) technique which recovers hyperspectral image(HSI) data from random projection efficiently, has been proved to be significant in decreasing signal-sensing costs at the sender. Inspired by the fact that the spectral signature of the same ground cover is similar, and two pixels of the nei...

This paper analyzes the classification of hyperspectral images with the sparse representation algorithm in the presence of a minimal reconstruction error. Incorporating the contextual information into the sparse recovery process can improve the classification performance. However, previous sparse algorithms using contextual information only assume...

We propose a spectral-spatial linear discriminant analysis method (LDA) for dimensionality reduction in hyperspectral image. The proposed method uses a local scatter of the small neighborhood as a regularizer to incorporate into the objective function of the LDA. The intrinsic idea is to design an optimal linear transformation that makes these samp...

Dimension reduction plays an important role in the community of high dimensional data analysis. The notion of random anisotropic transform (RAT), which was applied to speed up the computation procedure of dimension reduction kernel(DRK) with Isomap embedding (Isomap-RAT), was introduced in this paper. Nevertheless, traditional Isomap-RAT does not c...

The choice of the over-complete dictionary that sparsely represents data is of prime importance for sparse coding-based image super-resolution. Sparse coding is a typical unsupervised learning method to generate an over-complete dictionary. However, most of the sparse coding methods for image super-resolution fail to simultaneously consider the geo...

Learning an appropriate dictionary is a critical issue for sparse representation based super-resolution algorithms. A good dictionary can well represent an underlying image. In super-resolution algorithms, classical dictionary learning doesn't consider the information of test image which contains the information of the reconstructed image. In this...

Local learning algorithm has been widely used in single-frame super-resolution reconstruction algorithm, such as neighbor embedding algorithm [1] and locality preserving constraints algorithm [2]. Neighbor embedding algorithm is based on manifold assumption, which defines that the embedded neighbor patches are contained in a single manifold. While...

This paper presents a novel dictionary learning method for image denoising, which removes zero-mean independent identically distributed additive noise from a given image. Choosing noisy image itself to train an over-complete dictionary, the dictionary trained by traditional sparse coding methods contains noise information. Through mathematical deri...