Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

6 Scopus Citations
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Author(s)

  • Minye Wang
  • Xianyun Fei
  • Yuanzhi Zhang
  • Zhou Chen
  • Xiaoxue Wang
  • Dawei Liu
  • Xia Lu

Detail(s)

Original languageEnglish
Article number778
Journal / PublicationRemote Sensing
Volume10
Issue number5
Online published17 May 2018
Publication statusPublished - May 2018
Externally publishedYes

Abstract

Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. Based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pléiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images.

Research Area(s)

  • Coastal wetland vegetation, Completed local binary patterns (CLBP), Feature extraction, Object-based classification

Citation Format(s)

Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP). / Wang, Minye; Fei, Xianyun; Zhang, Yuanzhi; Chen, Zhou; Wang, Xiaoxue; Tsou, Jin Yeu; Liu, Dawei; Lu, Xia.

In: Remote Sensing, Vol. 10, No. 5, 778, 05.2018.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal