Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images

Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images


Joint spectral and spatial information should be fully exploited in order to achieve accurate classification results for hyperspectral images. In this paper, we propose an ensemble framework, which combines spectral and spatial information in different scales. The motivation of the proposed method derives from the basic idea: by integrating many individual learners, ensemble learning can achieve better generalization ability than a single learner. In the proposed work, the individual learners are obtained by joint spectral-spatial features generated from different scales. Specially, we develop two techniques to construct the ensemble model, namely, hierarchical guidance filtering (HGF) and matrix of spectral angle distance (mSAD). HGF and mSAD are combined via a weighted ensemble strategy. HGF is a hierarchical edge-preserving filtering operation, which could produce diverse sample sets. Meanwhile, in each hierarchy, a different spatial contextual information is extracted. With the increase of hierarchy, the pixels spectra tend smooth, while the spatial features are enhanced. Based on the outputs of HGF, a series of classifiers can be obtained. Subsequently, we define a low-rank matrix, mSAD, to measure the diversity among training samples in each hierarchy. Finally, an ensemble strategy is proposed using the obtained individual classifiers and mSAD. We term the proposed method as HiFi-We. Experiments are conducted on two popular data sets, Indian Pines and Pavia University, as well as a challenging hyperspectral data set used in 2014 Data Fusion Contest (GRSS_DFC_2014). An effectiveness analysis about the ensemble strategy is also displayed.


  • In existing paper, they proposed a novel method using independent component analysis (ICA) and edge-preserving filtering (EPF) via an ensemble strategy for the classification of hyperspectral data.
  • First, several subsets are randomly selected from the original feature space.
  • Second, ICA is used to extract spectrally independent components followed by an effective EPF method, to produce spatial features. Two strategies (i.e., parallel and concatenated) are presented to include the spatial features in the analysis.
  • The spectral-spatial features are then classified with a random forest or a rotation forest classifier.


  • In existing, they combined independent component analysis and EPF via an ensemble strategy. However, since EPF is still a kind of smoothing filtering method, it is difficult to determine what level of filtering is the most appropriate.
  • Stronger smoothing could result in better spatial representation, but at the same time lead to more loss of spectral information.


In this paper, we present a novel ensemble learning-based HSI classification method, which is composed of joint spectral-spatial features of different scales. First, in order to exploit the joint spectral-spatial information, we propose a hierarchical feature extraction strategy, hierarchical guidance filtering (HGF). HGF is an extension of guided filtering (GF) and rolling guidance filtering (RGF), which is able to generate a series of joint spectral-spatial features. Spatial contextual information of different scales is obtained by the filtering in different hierarchies. Second, instead of using complicated optimization techniques, we define a metric matrix, matrix of spectral angle distance (mSAD), to evaluate the feature quality in each hierarchy. Based on the obtained hierarchical features and the evaluation results, a popular ensemble strategy, the weighted voting, is employed to determine the final classification results. We term the proposed method as HiFi-We.

The initial motivations of this paper include two aspects. First, we want to combine the joint spectral-spatial information in different scales. The classification model should be determined from a more representative feature space. Second, spectral-spatial features extracted from different scales should have different contributions. More reliable and qualified features should get higher confidence. The solution comes as no surprise: we propose the HGF to obtain a series of spectral-spatial features from different scales; then, we design an ensemble model to simultaneously utilize these features; a new weighting method, mSAD, is also developed.

The major contributions of HiFi-We can be summarized as follows.

1) A new ensemble-based HSI classification method is proposed, where joint spectral-spatial information of different scales is combined.

2) We develop the HGF to extract more various spectral-spatial features.

3) The mSAD is designed and used to generate the weight coefficients in the ensemble model.


  • To evaluate the performance of the proposed method, we conduct contrast experiments with some state-of-the-art methods on two popular data sets and a challenging data set. The results indicate that the proposed method works well, and the effectiveness is verified via statistical evaluation.




  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’LED
  • Input Devices : Keyboard, Mouse
  • Ram :1GB


  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A


Bin Pan, Zhenwei Shi, Member, IEEE, and Xia Xu, “Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images”, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017.


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