Le Sun

Homogeneous region based low rank representation in hidden field for hyperspectral classification

In this paper, a new classifier under Bayesian framework is proposed to explore homogeneous region based low rank representation in hidden field for classification of hyperspectral imagery (HSI). This classifier integrates low rank representation and superpixel segmentation simultaneously, in which the HSI data is assumed to be lying in a low rank subspace within each homogeneous region of an estimated hidden field. First, the HSI data is projected into the Principal Component space, then the first principal component image is segmented into hundreds of homogeneous regions. Following, the spectral-only supervised Bayesian classifier, i.e., Sparse Multinomial Logistic Regression (SMLR), is utilized for estimating the likelihood probabilities of testing samples, then spatial information is exploited by low rank representation within each superpixel in a hidden field which is approximated to the pre-estimated likelihood probabilities. The proposed model can be easily solved by alternating direction method of multipliers (ADMM). Experimental results on real hyperspectral data, i.e., AVIRIS Indian Pines and ROSIS University of Pavia, show that the proposed classifier outperforms other state-of-the-art classifiers in terms of quantitative assessment and visual effect.

Hyperspectral unmixing employing l1−l2 sparsity and total variation regularization

ABSTRACT Hyperspectral unmixing is essential for image analysis and quantitative applications. To further improve the accuracy of hyperspectral unmixing, we propose a novel linear hyperspectral unmixing method based on l1−l2 sparsity and total variation (TV) regularization. First, the enhanced sparsity based on the l1−l2 norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in a sparse regression unmixing model because the l1−l2 norm promotes stronger sparsity than the l1 norm. Then, TV is minimized to enforce the spatial smoothness by considering the spatial correlation between neighbouring pixels. Finally, the extended alternating direction method of multipliers (ADMM) is utilized to solve the proposed model. Experimental results on simulated and real hyperspectral datasets show that the proposed method outperforms several state-of-the-art unmixing methods.

A Novel Feature-Based Text Classification Improving the Accuracy of Twitter Sentiment Analysis

With the growth of Internet and various online services, tremendous amount of data are generated in real time. As a result, sentiment analysis of online reviews has become an important research problem. In this paper a novel feature selection and weighting scheme is proposed for the sentiment analysis of twitter data. The Part of Speech (POS) tagging and Bayes-based Classifier are utilized in the proposed scheme. Also, different from the existing schemes, independency of the attributes and the influence of emotional words are properly manipulated in deciding the polarity of test data. Computer simulation with Sentiment 140 workload shows that the proposed scheme significantly outperforms the existing sentiment analysis schemes such as naive Bayes classifier and selective Bayes classifier.

Hyperspectral image classification using multilayer superpixel graph and loopy belief propagation

In this paper, we propose a new method for hyperspectral image (HSI) classification using multi-layer superpixel graph and loopy belief propagation. A merging algorithm using graph based representation of image is applied to generate multi-scale superpixels in hyperspectral image at first. Then, we build a multi-layer superpixel graph and use loopy belief propagation to transmit messages between the superpixels and compute beliefs at each superpixel in our multi-layer graph for HSI classification. Experimental results with real hyperspectral data set demonstrate that our proposed method provides good performance and is competitive with some of the best available spectral-spatial methods for hyperspectral image classification.

Hyperspectral image classification using multinomial logistic regression and non-local prior on hidden fields

In this paper, we present a supervised hyperspectral image segmentation method based on multinomial logistic regression and a convex formulation of a marginal maximum a posteriori (MAP) segmentation with non-local total variation prior on the hidden fields under Bayesian framework. It not only exploits the basic assumption that samples within each class approximately lie in a lower dimensional subspace, but also sidesteps the discrete nature of the image segmentation problems by modeling spatial prior with vectorial non local means on the hidden fields. Alternating direction method of multipliers (ADMM) is finally extended to solve the proposed model. The proposed algorithm is validated by real hyperspectral data set.

Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to achieve good classification performance. In this paper, multiple kernel learning-based low rank representation at superpixel level (Sp_MKL_LRR) is proposed to improve the classification accuracy for hyperspectral images. Superpixels are generated first from the hyperspectral image to reduce noise effect and form homogeneous regions. An optimal superpixel kernel parameter is then selected by the kernel matrix using a multiple kernel learning framework. Finally, a kernel low rank representation is applied to classify the hyperspectral image. The proposed method offers two advantages. (1) The global correlation constraint is exploited by the low rank representation, while the local neighborhood information is extracted as the superpixel kernel adaptively learns the high-dimensional manifold features of the samples in each class; (2) It can meet the challenges of multiscale feature learning and adaptive parameter determination in the conventional kernel methods. Experimental results on several hyperspectral image datasets demonstrate that the proposed method outperforms several state-of-the-art classifiers tested in terms of overall accuracy, average accuracy, and kappa statistic.

A Novel Subspace Super-Pixel Based Low Rank Representation Method for Hyperspectral Denoising

This paper presents a novel denoising method based on subspace superpixel based low rank representation for hyperspectral imagery. First, the original hyperspectral data is assumed to be low-rank in both spectral and spatial domains. The spectral low rankness of HSI data is represented by decomposing it into two sub-matrices of lower rank while the spatial low rankness is explored within superpixel based regions in the subspace. The superpixels are generated by utilizing state-of-the-art superpixel segmentation algorithms in the first principle component of the original HSI. The final model could be efficiently solved by augmented Lagrangian method (ALM). Experimental results on simulated hyperspectral dataset validate that the proposed method produces superior performance than other state-of-the-art denoising methods in terms of quantitative assessment and visual quality.

Hyperspectral unmixing based on L1-L2 sparsity and total variation

This paper proposes a novel linear hyperspectral unmixing method based on l1-l2 sparsity and total variation (TV) regularization. First, the enhanced sparsity based on l1-l2 norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in sparse regression unmixing model. By taking the correlation between hyperspectral pixels into account, total variation is minimized to enforce the spatial smoothness. Finally, the proposed model is solved by the extended alternating direction method of multipliers (ADMM). Experimental results on simulated and real hyperspectral datasets validate the excellent performances of the proposed method.