Mr. Lixin Sun Profile

Lixin Sun

at Canada Ctr for Remote Sensing

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Author

Publications (5)

Proceedings Article | 27 August 2008 Paper

Improved iterative error analysis for endmember extraction from hyperspectral imagery

Lixin Sun, Ying Zhang, Bert Guindon

Proceedings Volume 7086, 70860S (2008) https://doi.org/10.1117/12.799232

KEYWORDS: Hyperspectral imaging, Feature extraction, Image processing, Error analysis, Remote sensing, Algorithm development, Shape memory alloys, Spectroscopy, Evolutionary algorithms, Image quality

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Automated image endmember extraction from hyperspectral imagery is a challenge and a critical step in spectral mixture analysis (SMA). Over the past years, great efforts were made and a large number of algorithms have been proposed to address this issue. Iterative error analysis (IEA) is one of the well-known existing endmember extraction methods. IEA identifies pixel spectra as a number of image endmembers by an iterative process. In each of the iterations, a fully constrained (abundance nonnegativity and abundance sum-to-one constraints) spectral unmixing based on previously identified endmembers is performed to model all image pixels. The pixel spectrum with the largest residual error is then selected as a new image endmember. This paper proposes an updated version of IEA by making improvements on three aspects of the method. First, fully constrained spectral unmixing is replaced by a weakly constrained (abundance nonnegativity and abundance sum-less-or-equal-to-one constraints) alternative. This is necessary due to the fact that only a subset of endmembers exhibit in a hyperspectral image have been extracted up to an intermediate iteration and the abundance sum-to-one constraint is invalid at the moment. Second, the search strategy for achieving an optimal set of image endmembers is changed from sequential forward selection (SFS) to sequential forward floating selection (SFFS) to reduce the so-called "nesting effect" in resultant set of endmembers. Third, a pixel spectrum is identified as a new image endmember depending on both its spectral extremity in the feature hyperspace of a dataset and its capacity to characterize other mixed pixels. This is achieved by evaluating a set of extracted endmembers using a criterion function, which is consisted of the mean and standard deviation of residual error image. Preliminary comparison between the image endmembers extracted using improved and original IEA are conducted based on an airborne visible infrared imaging spectrometer (AVIRIS) dataset acquired over Cuprite mining district, Nevada, USA.

Proceedings Article | 12 August 2004 Paper

Detection of keystone in imaging spectrometer data

Robert Neville, Lixin Sun, Karl Staenz

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.542806

KEYWORDS: Spectroscopy, Sensors, Data acquisition, Auroras, Imaging systems, Short wave infrared radiation, Detector arrays, Remote sensing, Satellites, Image sensors

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Proceedings Article | 23 September 2003 Paper

Detection of spectral line curvature in imaging spectrometer data

Robert Neville, Lixin Sun, Karl Staenz

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.487342

KEYWORDS: Sensors, Absorption, Reflectivity, Calibration, Spectroscopy, Short wave infrared radiation, Atmospheric corrections, Spectrographs, Oxygen, Data acquisition

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Proceedings Article | 17 August 1998 Paper

Method of selecting the best classification bands from hyperspectral images based on genetic algorithm and rough set

Lixin Sun, Wen Gao

Proceedings Volume 3502, (1998) https://doi.org/10.1117/12.317782

KEYWORDS: Genetic algorithms, Hyperspectral imaging, Image classification, Dimension reduction, Machine learning, Remote sensing, Feature selection, Genetics, Image filtering, Data processing

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Proceedings Article | 17 August 1998 Paper

Acquiring hyperspectral remotely sensed image classification rules using inductive learning

Lixin Sun, Yimei Zhang

Proceedings Volume 3502, (1998) https://doi.org/10.1117/12.317795

KEYWORDS: Image classification, Binary data, Matrices, Hyperspectral imaging, Genetic algorithms, Data analysis, Remote sensing, Algorithms, Sun, Ecosystems

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