Spectral and spatial classification of hyperspectral image

based on random multi-graphs

Submitted to Remote Sensing Letters

Feng Gao, Huizhen Yang, Junyu Dong, Qin Zhang

Last modified: 2018/1/15

Abstract: Classification of hyperspectral images have been well acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundant spectral and spatial information has provided great opportunity to effectively characterize and identify the ground material. In this letter, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs. First, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. Second, the high dimensional vectors are fed into the Random Multi-Graphs for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve satisfying classification performance. The experiments on two real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than four closely related methods.

[ MATLAB code ]

The demo has not been well organized. Please contact me if you meet any problems.

Links to most related works

  1. Benediktsson, J., Palmason, J., Sveinsson, J., 2005. Classification of hyperspectral data from urban areas based on extended morphological profiles, IEEE Transactions on Geoscience and Remote Sensing 43 (3): 480-490.
  2. Butz, C. et al., 2015. Hyperspectral imaging spectroscopy: a promising method for the biogeochemical analysis of lake sediments. Journal of Applied Remote Sensing 9 (1): 096031.
  3. Camps-Valls, G., Tuia, G., Bruzzone, L., 2014. Advances in hyperspectral image classification," IEEE Signal Processing Magine 31 (1): 45-54.
  4. Chen, Y., Zhao, X., Jia, X., 2015. Spectral-spatial classification of hyperspectral data based on deep belief network, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 8 (6): 2381-2392.
  5. Du, Q., Yang, H., 2008. Similarity-based unsupervised band selection for hyperspectral image analysis, IEEE Geoscience and Remote Sensing Letters 5 (4): 564-568.

>>   return to Feng Gao's homepage 

Last modified: 2018/1/15.