Spectral-spatial classification for hyperspectral image by bilateral filtering and morphological features

Wenzhi Liao, Daniel Erick Ochoa Donoso, Frieke Van Coillie, Jie Li, Chun Qi, Sidharta Gautama, Wilfried Philips

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)


Hyperspectral (HS) imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Conventional spectral-spatial classification methods cannot fully exploit both spectral and spatial information of HS image. In this paper, we propose a new method to fuse the spectral and spatial information for HS image classification. Our approach transfers the spatial structures of the whole morphological profile into the original HS image by using bilateral filtering, and obtains an enhanced HS image enriching both spectral and spatial information. Meanwhile, the enhanced HS image has the same spectral and spatial dimensions as the original HS image, which may provide a new input to improve the performances of existing HS image classification methods. Experimental results on real HS images are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed fusion method improves the overall classification accuracy more than 10% and 5%, respectively.
Original languageEnglish
Number of pages4
Publication statusPublished - 19 Oct 2017
Event2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Los Angeles, United States
Duration: 21 Aug 201624 Aug 2016


Workshop2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Abbreviated titleWHISPERS 2016
CountryUnited States
CityLos Angeles


  • hyperspectral imaging
  • stacking
  • principal component analysis
  • shape
  • asphalt
  • data fusion
  • mathematical morphology
  • bilateral filtering
  • feature extraction
  • image classification
  • image filtering
  • object detection

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