Selected work

Representative Papers

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement

Aug 06, 2026

Extreme low-light remote sensing images are highly susceptible to noise and illumination degradation. Existing methods often suffer from attention drift, leading to erroneous cross-boundary feature aggregation that causes structural blurring and color distortion. To address this, this work proposes the HALO framework, which formulates image enhancement as a guided feature aggregation problem. HALO introduces, for the first time, a joint mechanism comprising a semantic homogeneity-induced positive bias and a pseudo-3D topological heterogeneity-based negative penalty. This design is realized through a Homogeneity–Heterogeneity Collaborative Attention Module (H2CAM) that effectively suppresses inter-boundary confusion. Evaluated on eight synthetic and real-world remote sensing datasets, the proposed method significantly improves edge sharpness and color fidelity while better preserving discriminative features critical for downstream Earth observation tasks.

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Highly robust factored principal component analysis for matrix-valued outlier accommodation and explainable detection via matrix minimum covariance determinant

Sep 30, 2025

Traditional PCA fails for matrix-variate data under heavy-tailed distributions or contamination by outliers—due to structural distortion from vectorization and inherent sensitivity to anomalies. To address this, we propose High-Robustness Factor Principal Component Analysis (HRFPCA). Methodologically, HRFPCA is the first to embed the Matrix Minimum Covariance Determinant (MMCD) estimator into a factor PCA framework, jointly modeling row- and column-wise covariances under the matrix normal distribution—replacing the contamination-prone maximum likelihood estimator (MLE). It further integrates Score-Orthogonal Distance Analysis (SODA) for interpretable outlier localization and classification. HRFPCA achieves a breakdown point of nearly 50%, strictly preserves intrinsic two-dimensional matrix structure, and balances strong robustness with computational efficiency. Experiments on both synthetic and real-world datasets demonstrate that HRFPCA significantly outperforms state-of-the-art methods in outlier detection accuracy, robustness to contamination, and generalization capability.

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Matrix Healy Plot: A Practical Tool for Visual Assessment of Matrix-Variate Normality

May 01, 2025

This paper addresses the challenge of normality testing for matrix-valued data, proposing Matrix Healy (MHealy) plots—a novel visualization method that overcomes sample-size limitations. Conventional Distance–Distance (DD) plots require vectorization of matrices, rendering them inapplicable when the vectorized dimension exceeds the sample size. In contrast, MHealy plots directly define the squared Mahalanobis distance on the matrix manifold, bypassing vectorization entirely and enabling reliable graphical diagnosis of matrix normality even in small-sample, high-dimensional settings. The method integrates matrix differential geometry, matrix normal distribution theory, and a matrix-domain extension of the Healy plot paradigm. Empirical evaluations demonstrate that MHealy plots significantly outperform DD plots under low-sample-size and high-dimensional conditions, while maintaining robustness, interpretability, and practical utility.

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Recent publications

Latest Papers

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement

Aug 06, 2026

Extreme low-light remote sensing images are highly susceptible to noise and illumination degradation. Existing methods often suffer from attention drift, leading to erroneous cross-boundary feature aggregation that causes structural blurring and color distortion. To address this, this work proposes the HALO framework, which formulates image enhancement as a guided feature aggregation problem. HALO introduces, for the first time, a joint mechanism comprising a semantic homogeneity-induced positive bias and a pseudo-3D topological heterogeneity-based negative penalty. This design is realized through a Homogeneity–Heterogeneity Collaborative Attention Module (H2CAM) that effectively suppresses inter-boundary confusion. Evaluated on eight synthetic and real-world remote sensing datasets, the proposed method significantly improves edge sharpness and color fidelity while better preserving discriminative features critical for downstream Earth observation tasks.

0 citationsRead paper

Highly robust factored principal component analysis for matrix-valued outlier accommodation and explainable detection via matrix minimum covariance determinant

Sep 30, 2025

Traditional PCA fails for matrix-variate data under heavy-tailed distributions or contamination by outliers—due to structural distortion from vectorization and inherent sensitivity to anomalies. To address this, we propose High-Robustness Factor Principal Component Analysis (HRFPCA). Methodologically, HRFPCA is the first to embed the Matrix Minimum Covariance Determinant (MMCD) estimator into a factor PCA framework, jointly modeling row- and column-wise covariances under the matrix normal distribution—replacing the contamination-prone maximum likelihood estimator (MLE). It further integrates Score-Orthogonal Distance Analysis (SODA) for interpretable outlier localization and classification. HRFPCA achieves a breakdown point of nearly 50%, strictly preserves intrinsic two-dimensional matrix structure, and balances strong robustness with computational efficiency. Experiments on both synthetic and real-world datasets demonstrate that HRFPCA significantly outperforms state-of-the-art methods in outlier detection accuracy, robustness to contamination, and generalization capability.

0 citationsRead paper

Matrix Healy Plot: A Practical Tool for Visual Assessment of Matrix-Variate Normality

May 01, 2025

This paper addresses the challenge of normality testing for matrix-valued data, proposing Matrix Healy (MHealy) plots—a novel visualization method that overcomes sample-size limitations. Conventional Distance–Distance (DD) plots require vectorization of matrices, rendering them inapplicable when the vectorized dimension exceeds the sample size. In contrast, MHealy plots directly define the squared Mahalanobis distance on the matrix manifold, bypassing vectorization entirely and enabling reliable graphical diagnosis of matrix normality even in small-sample, high-dimensional settings. The method integrates matrix differential geometry, matrix normal distribution theory, and a matrix-domain extension of the Healy plot paradigm. Empirical evaluations demonstrate that MHealy plots significantly outperform DD plots under low-sample-size and high-dimensional conditions, while maintaining robustness, interpretability, and practical utility.

0 citationsRead paper