@InProceedings{8553223,
  author = {Z. Li and J. Chen and S. Rahardja},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Superpixel construction for hyperspectral unmixing},
  year = {2018},
  pages = {647-651},
  abstract = {Spectral unmixing aims to determine the component materials and their associated abundances from mixed pixels in a hyperspectral image. Instead of performing unmixing independently on each pixel, investigating spatial and spectral correlations among pixels can be beneficial to enhance the unmixing performance. However linking pixels across an entire image for such a purpose can be computationally cumbersome and physically unreasonable. In order to address this issue, we propose to construct superpixels for hyperspectral data unmixing. Using an SLIC-based (Simple Linear Iterative Clustering) superpixel constructing process, adjacent pixels are clustered into several blocks with similar spectral signatures. After this preprocessing, unmixing is then performed with a graph-based total variation regularization to benefit from the heterogeneity within each superpixel. Experimental results on synthetic data and real hyperspectral data illustrate advantages of the proposed scheme.},
  keywords = {graph theory;hyperspectral imaging;image segmentation;iterative methods;spatial correlations;spectral correlations;hyperspectral data unmixing;adjacent pixels;graph-based total variation regularization;superpixel construction;hyperspectral unmixing;simple linear iterative clustering;spectral signatures;SLIC-based superpixel constructing process;Hyperspectral imaging;Signal processing algorithms;Estimation;Europe;Signal processing;Correlation;Hyperspectral images;spectral unmixing;super-pixel analysis;graph regularization},
  doi = {10.23919/EUSIPCO.2018.8553223},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438996.pdf},
}

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However linking pixels across an entire image for such a purpose can be computationally cumbersome and physically unreasonable. In order to address this issue, we propose to construct superpixels for hyperspectral data unmixing. Using an SLIC-based (Simple Linear Iterative Clustering) superpixel constructing process, adjacent pixels are clustered into several blocks with similar spectral signatures. After this preprocessing, unmixing is then performed with a graph-based total variation regularization to benefit from the heterogeneity within each superpixel. Experimental results on synthetic data and real hyperspectral data illustrate advantages of the proposed scheme.","keywords":"graph theory;hyperspectral imaging;image segmentation;iterative methods;spatial correlations;spectral correlations;hyperspectral data unmixing;adjacent pixels;graph-based total variation regularization;superpixel construction;hyperspectral unmixing;simple linear iterative clustering;spectral signatures;SLIC-based superpixel constructing process;Hyperspectral imaging;Signal processing algorithms;Estimation;Europe;Signal processing;Correlation;Hyperspectral images;spectral unmixing;super-pixel analysis;graph regularization","doi":"10.23919/EUSIPCO.2018.8553223","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438996.pdf","bibtex":"@InProceedings{8553223,\n author = {Z. Li and J. Chen and S. Rahardja},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Superpixel construction for hyperspectral unmixing},\n year = {2018},\n pages = {647-651},\n abstract = {Spectral unmixing aims to determine the component materials and their associated abundances from mixed pixels in a hyperspectral image. Instead of performing unmixing independently on each pixel, investigating spatial and spectral correlations among pixels can be beneficial to enhance the unmixing performance. However linking pixels across an entire image for such a purpose can be computationally cumbersome and physically unreasonable. In order to address this issue, we propose to construct superpixels for hyperspectral data unmixing. Using an SLIC-based (Simple Linear Iterative Clustering) superpixel constructing process, adjacent pixels are clustered into several blocks with similar spectral signatures. After this preprocessing, unmixing is then performed with a graph-based total variation regularization to benefit from the heterogeneity within each superpixel. Experimental results on synthetic data and real hyperspectral data illustrate advantages of the proposed scheme.},\n keywords = {graph theory;hyperspectral imaging;image segmentation;iterative methods;spatial correlations;spectral correlations;hyperspectral data unmixing;adjacent pixels;graph-based total variation regularization;superpixel construction;hyperspectral unmixing;simple linear iterative clustering;spectral signatures;SLIC-based superpixel constructing process;Hyperspectral imaging;Signal processing algorithms;Estimation;Europe;Signal processing;Correlation;Hyperspectral images;spectral unmixing;super-pixel analysis;graph regularization},\n doi = {10.23919/EUSIPCO.2018.8553223},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438996.pdf},\n}\n\n","author_short":["Li, Z.","Chen, J.","Rahardja, S."],"key":"8553223","id":"8553223","bibbaseid":"li-chen-rahardja-superpixelconstructionforhyperspectralunmixing-2018","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438996.pdf"},"keyword":["graph theory;hyperspectral imaging;image segmentation;iterative methods;spatial correlations;spectral correlations;hyperspectral data unmixing;adjacent pixels;graph-based total variation regularization;superpixel construction;hyperspectral unmixing;simple linear iterative clustering;spectral signatures;SLIC-based superpixel constructing process;Hyperspectral imaging;Signal processing algorithms;Estimation;Europe;Signal processing;Correlation;Hyperspectral images;spectral unmixing;super-pixel analysis;graph regularization"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2018url.bib","creationDate":"2021-02-13T15:38:40.339Z","downloads":0,"keywords":["graph theory;hyperspectral imaging;image segmentation;iterative methods;spatial correlations;spectral correlations;hyperspectral data unmixing;adjacent pixels;graph-based total variation regularization;superpixel construction;hyperspectral unmixing;simple linear iterative clustering;spectral signatures;slic-based superpixel constructing process;hyperspectral imaging;signal processing algorithms;estimation;europe;signal processing;correlation;hyperspectral images;spectral unmixing;super-pixel analysis;graph regularization"],"search_terms":["superpixel","construction","hyperspectral","unmixing","li","chen","rahardja"],"title":"Superpixel construction for hyperspectral unmixing","year":2018,"dataSources":["yiZioZximP7hphDpY","iuBeKSmaES2fHcEE9"]}