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Generalized differential morphological profiles for remote sensing image classification

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Abstract
Differential morphological profiles (DMPs) are widely used for the spatial/structural feature extraction and classification of remote sensing images. They can be regarded as the shape spectrum, depicting the response of the image structures related to different scales and sizes of the structural elements (SEs). DMPs are defined as the difference of morphological profiles (MPs) between consecutive scales. However, traditional DMPs can ignore discriminative information for features that are across the scales in the profiles. To solve this problem, we propose scale-span differential profiles, i.e., generalized DMPs (GDMPs), to obtain the entire differential profiles. GDMPs can describe the complete shape spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics and complex landscapes of remote sensing image scenes. Subsequently, the random forest (RF) classifier is applied to interpret GDMPs considering its robustness for high-dimensional data and ability of evaluating the importance of variables. Meanwhile, the RF "out-of-bag" error can be used to quantify the importance of each channel of GDMPs and select the most discriminative information in the entire profiles. Experiments conducted on three well-known hyperspectral data sets as well as an additional World View-2 data are used to validate the effectiveness of GDMPs compared to the traditional DMPs. The results are promising as GDMPs can significantly outperform the traditional one, as it is capable of adequately exploring the multiscale morphological information.
Keywords
Random Forest (RF), Morphological profiles, feature selection, classification., feature extraction, SUPERVISED FEATURE-EXTRACTION, LOCAL BINARY PATTERNS, URBAN AREAS, SPATIAL CLASSIFICATION, ATTRIBUTE PROFILES, HYPERSPECTRAL DATA

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MLA
Huang, Xin, et al. “Generalized Differential Morphological Profiles for Remote Sensing Image Classification.” IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, edited by Jocelyn Chanussot, vol. 9, no. 4, 2016, pp. 1736–51.
APA
Huang, X., Han, X., Zhang, L., Gong, J., Liao, W., & Benediktsson, J. A. (2016). Generalized differential morphological profiles for remote sensing image classification. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 9(4), 1736–1751.
Chicago author-date
Huang, Xin, Xiaopeng Han, Liangpei Zhang, Jianya Gong, Wenzhi Liao, and Jon Atli Benediktsson. 2016. “Generalized Differential Morphological Profiles for Remote Sensing Image Classification.” Edited by Jocelyn Chanussot. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 9 (4): 1736–51.
Chicago author-date (all authors)
Huang, Xin, Xiaopeng Han, Liangpei Zhang, Jianya Gong, Wenzhi Liao, and Jon Atli Benediktsson. 2016. “Generalized Differential Morphological Profiles for Remote Sensing Image Classification.” Ed by. Jocelyn Chanussot. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 9 (4): 1736–1751.
Vancouver
1.
Huang X, Han X, Zhang L, Gong J, Liao W, Benediktsson JA. Generalized differential morphological profiles for remote sensing image classification. Chanussot J, editor. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. 2016;9(4):1736–51.
IEEE
[1]
X. Huang, X. Han, L. Zhang, J. Gong, W. Liao, and J. A. Benediktsson, “Generalized differential morphological profiles for remote sensing image classification,” IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 9, no. 4, pp. 1736–1751, 2016.
@article{7064981,
  abstract     = {Differential morphological profiles (DMPs) are widely used for the spatial/structural feature extraction and classification of remote sensing images. They can be regarded as the shape spectrum, depicting the response of the image structures related to different scales and sizes of the structural elements (SEs). DMPs are defined as the difference of morphological profiles (MPs) between consecutive scales. However, traditional DMPs can ignore discriminative information for features that are across the scales in the profiles. To solve this problem, we propose scale-span differential profiles, i.e., generalized DMPs (GDMPs), to obtain the entire differential profiles. GDMPs can describe the complete shape spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics and complex landscapes of remote sensing image scenes. Subsequently, the random forest (RF) classifier is applied to interpret GDMPs considering its robustness for high-dimensional data and ability of evaluating the importance of variables. Meanwhile, the RF "out-of-bag" error can be used to quantify the importance of each channel of GDMPs and select the most discriminative information in the entire profiles. Experiments conducted on three well-known hyperspectral data sets as well as an additional World View-2 data are used to validate the effectiveness of GDMPs compared to the traditional DMPs. The results are promising as GDMPs can significantly outperform the traditional one, as it is capable of adequately exploring the multiscale morphological information.},
  author       = {Huang, Xin and Han, Xiaopeng and Zhang, Liangpei and Gong, Jianya and Liao, Wenzhi and Benediktsson, Jon Atli},
  editor       = {Chanussot, Jocelyn},
  issn         = {1939-1404},
  journal      = {IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING},
  keywords     = {Random Forest (RF),Morphological profiles,feature selection,classification.,feature extraction,SUPERVISED FEATURE-EXTRACTION,LOCAL BINARY PATTERNS,URBAN AREAS,SPATIAL CLASSIFICATION,ATTRIBUTE PROFILES,HYPERSPECTRAL DATA},
  language     = {eng},
  number       = {4},
  pages        = {1736--1751},
  title        = {Generalized differential morphological profiles for remote sensing image classification},
  url          = {http://dx.doi.org/10.1109/JSTARS.2016.2524586},
  volume       = {9},
  year         = {2016},
}

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