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Morphological attribute profiles with partial reconstruction

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FWO and IWT
Abstract
Extended attribute profiles (EAPs) have been widely used for the classification of high-resolution hyperspectral images. EAPs are obtained by computing a sequence of attribute operators. Attribute filters (AFs) are connected operators, so they can modify an image by only merging its flat zones. These filters are effective when dealing with very high resolution images since they preserve the geometrical characteristics of the regions that are not removed from the image. However, AFs, being connected filters, suffer the problem of “leakage” (i.e., regions related to different structures in the image that happen to be connected by spurious links will be considered as a single object). Objects expected to disappear at a certain threshold remain present when they are connected with other objects in the image. The attributes of small objects will be mixed with their larger connected objects. In this paper, we propose a novel framework for morphological AFs with partial reconstruction and extend it to the classification of high-resolution hyperspectral images. The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification. An important characteristic of the presented approach is that it is very robust to the ranges of rescaled principal components, as well as the selection of attribute values. Our experimental results, conducted using a variety of hyperspectral images, indicate that the proposed framework for AFs with partial reconstruction provides state-of-the-art classification results. Compared to the methods using only single EAP and stacking all EAPs computed by existing attribute opening and closing together, the proposed framework benefits significant improvements in overall classification accuracy.
Keywords
morphlogical filter, hyperspectral image, spatial information modelling, Remote sensing, HYPERSPECTRAL IMAGE CLASSIFICATION, SUPERVISED FEATURE-EXTRACTION, SPATIAL CLASSIFICATION, FILTERS, ALGORITHMS, REDUCTION, OPERATORS, SELECTION, ACCURACY, FUSION

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Citation

Please use this url to cite or link to this publication:

MLA
Liao, Wenzhi et al. “Morphological Attribute Profiles with Partial Reconstruction.” Ed. Antonio J Plaza. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 54.3 (2016): 1738–1756. Print.
APA
Liao, W., Dalla Mura, M., Chanussot, J., Bellens, R., & Philips, W. (2016). Morphological attribute profiles with partial reconstruction. (A. J. Plaza, Ed.)IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 54(3), 1738–1756.
Chicago author-date
Liao, Wenzhi, Mauro Dalla Mura, Jocelyn Chanussot, Rik Bellens, and Wilfried Philips. 2016. “Morphological Attribute Profiles with Partial Reconstruction.” Ed. Antonio J Plaza. Ieee Transactions on Geoscience and Remote Sensing 54 (3): 1738–1756.
Chicago author-date (all authors)
Liao, Wenzhi, Mauro Dalla Mura, Jocelyn Chanussot, Rik Bellens, and Wilfried Philips. 2016. “Morphological Attribute Profiles with Partial Reconstruction.” Ed. Antonio J Plaza. Ieee Transactions on Geoscience and Remote Sensing 54 (3): 1738–1756.
Vancouver
1.
Liao W, Dalla Mura M, Chanussot J, Bellens R, Philips W. Morphological attribute profiles with partial reconstruction. Plaza AJ, editor. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. IEEE; 2016;54(3):1738–56.
IEEE
[1]
W. Liao, M. Dalla Mura, J. Chanussot, R. Bellens, and W. Philips, “Morphological attribute profiles with partial reconstruction,” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 54, no. 3, pp. 1738–1756, 2016.
@article{7011472,
  abstract     = {Extended attribute profiles (EAPs) have been widely used for the classification of high-resolution hyperspectral images. EAPs are obtained by computing a sequence of attribute operators. Attribute filters (AFs) are connected operators, so they can modify an image by only merging its flat zones. These filters are effective when dealing with very high resolution images since they preserve the geometrical characteristics of the regions that are not removed from the image. However, AFs, being connected filters, suffer the problem of “leakage” (i.e., regions related to different structures in the image that happen to be connected by spurious links will be considered as a single object). Objects expected to disappear at a certain threshold remain present when they are connected with other objects in the image. The attributes of small objects will be mixed with their larger connected objects. In this paper, we propose a novel framework for morphological AFs with partial reconstruction and extend it to the classification of high-resolution hyperspectral images. The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification. An important characteristic of the presented approach is that it is very robust to the ranges of rescaled principal components, as well as the selection of attribute values. Our experimental results, conducted using a variety of hyperspectral images, indicate that the proposed framework for AFs with partial reconstruction provides state-of-the-art classification results. Compared to the methods using only single EAP and stacking all EAPs computed by existing attribute opening and closing together, the proposed framework benefits significant improvements in overall classification accuracy.},
  author       = {Liao, Wenzhi and Dalla Mura, Mauro and Chanussot, Jocelyn  and Bellens, Rik and Philips, Wilfried},
  editor       = {Plaza, Antonio J},
  issn         = {0196-2892},
  journal      = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
  keywords     = {morphlogical filter,hyperspectral image,spatial information modelling,Remote sensing,HYPERSPECTRAL IMAGE CLASSIFICATION,SUPERVISED FEATURE-EXTRACTION,SPATIAL CLASSIFICATION,FILTERS,ALGORITHMS,REDUCTION,OPERATORS,SELECTION,ACCURACY,FUSION},
  language     = {eng},
  number       = {3},
  pages        = {1738--1756},
  publisher    = {IEEE},
  title        = {Morphological attribute profiles with partial reconstruction},
  url          = {http://dx.doi.org/10.1109/TGRS.2015.2488280},
  volume       = {54},
  year         = {2016},
}

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