Prof. Keng-Hao Liu Profile

Prof. Keng-Hao Liu

Assistant Professor at National Sun Yat-Sen Univ.

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Publications (6)

Proceedings Article | 10 October 2017 Paper

Progressive sample processing of band selection for hyperspectral imagery

Keng-Hao Liu, Hung-Chang Chien, Shih-Yu Chen

Proceedings Volume 10427, 104271L (2017) https://doi.org/10.1117/12.2278174

KEYWORDS: Hyperspectral imaging, Data transmission, Image processing

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Proceedings Article | 8 November 2012 Paper

Real-time progressive band processing of linear spectral unmixing

Chao-Cheng Wu, Keng-hao Liu, Chein-I Chang

Proceedings Volume 8539, 853904 (2012) https://doi.org/10.1117/12.979180

KEYWORDS: Data communications, Spectral data processing, Data processing, Thallium, Data compression, Satellite communications, Remote sensing, Image processing, Data analysis, Filtering (signal processing)

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Proceedings Article | 20 May 2011 Paper

Kernel-based weighted abundance constrained linear spectral mixture analysis

Keng-Hao Liu, Englin Wong, Chein-I Chang

Proceedings Volume 8048, 80481P (2011) https://doi.org/10.1117/12.884650

KEYWORDS: Vegetation, Matrices, Buildings, Roads, Hyperspectral imaging, Multispectral imaging, Remote sensing, Image classification, Mahalanobis distance, Satellite imaging

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Proceedings Article | 20 May 2011 Paper

Dynamic dimensionality reduction for hyperspectral imagery

Haleh Safavi, Keng-Hao Liu, Chein-I Chang

Proceedings Volume 8048, 80481I (2011) https://doi.org/10.1117/12.884649

KEYWORDS: Principal component analysis, Photonic integrated circuits, Independent component analysis, Hyperspectral imaging, Statistical analysis, Data analysis, Image classification, Data modeling, Data processing, Data compression

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Proceedings Article | 31 August 2009 Paper

Progressive dimensionality reduction for hyperspectral imagery

Haleh Safavi, Keng-Hao Liu, Chein-I Chang

Proceedings Volume 7455, 745508 (2009) https://doi.org/10.1117/12.828367

KEYWORDS: Photonic integrated circuits, Principal component analysis, Independent component analysis, Hyperspectral imaging, Data processing, Algorithm development, Detection and tracking algorithms, Target detection, Data compression, Projection systems

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Proceedings Article | 2 May 2008 Paper

Exploration of component analysis in multi/hyperspectral image processing

Keng-Hao Liu, Chein-I Chang

Proceedings Volume 6966, 69661K (2008) https://doi.org/10.1117/12.782219

KEYWORDS: Principal component analysis, Independent component analysis, Image classification, Roads, Multispectral imaging, Buildings, Vegetation, Data analysis, Hyperspectral imaging, Nickel

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Component Analysis (CA) has found many applications in remote sensing image processing. Two major component analyses are of particular interest, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) which have been widely used in signal processing. While the PCA de-correlates data samples via 2nd order statistics in a set of Principal Components (PCs), the ICA represents data samples via statistical independency in a set of statistically Independent Components (ICs). However, in order to for component analyses to be effective, the number of components to be generated, p must be sufficient for data analysis. Unfortunately, in MultiSpectral Imagery (MSI) p seems to be small, while p in HyperSpectral Imagery (HSI) seems too large. Interestingly, very little has been reported on how to deal with this issue when p is too small or too large. This paper investigates this issue. When p is too small, two approaches are developed to mitigate the problem. One is Band Expansion Process (BEP) which augments original data band dimensionality by producing additional bands via a set of nonlinear functions. The other is a kernel-based approach, referred to as Kernel-based PCA (K-PCA) which maps features in the original data space to a higher dimensional feature space via a set of nonlinear kernel. While both approaches make attempts to resolve the issue of a small p using a set of nonlinear functions, their design rationales are completely different, particularly they are not correlated. As for a large p such as HSI, a recently developed Virtual Dimensionality (VD) can be used for this purpose where the VD was originally developed to estimate number of spectrally distinct signatures. If we assume one spectrally distinct signature can be accommodated by one component, the value of p can be actually determined by the VD. Finally, experiments are conducted to explore and evaluate the utility of component analyses, specifically, PCA and ICA using BEP and K-PCA for MSI and VD for HSI.

Showing 5 of 6 publications

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