Objective

2

Hyperspectral imagery contains rich spectral and spatial information and transforms remote sensing technology from qualitative to quantitative analysis. It can be widely used in geological prospecting

precision agriculture

ecology environment

and urban remote sensing. However

classification and recognition applications have many difficulties because hyperspectral image has large amount of data

multiple bands

and strong correlation between bands. In particular

the "Hughes" phenomenon caused by the decline in classification accuracy with the increase in data complexity when the number of training samples is limited needs to be addressed. Research on feature extraction and classification algorithm for hyperspectral image has become an important task in hyperspectral data processing to fully utilize the advantages of hyperspectral remote sensing technology and overcome the disadvantages caused by the large number of bands. Multi-classifier system with band-selection dimensionality reduction is effective for hyperspectral image classification

particularly when the size of the training dataset is small. The usual methods of decision fusion strategies at present are hard decision fusion represented by majority vote (MV) and soft decision fusion represented by logarithmic opinion pool (LOGP). These methods use the unified weight coefficient for decision fusion without evaluating the respective classification performance of sub-classifiers and optimizing the allocation of weight coefficients

which will inevitably affect the classification results. Two adaptive decision fusion strategies are studied in this work on the basis of MV and LOGP to solve this problem

and a multi-classifier system is designed for hyperspectral image classification.

Method

2

The correlation between adjacent bands of hyperspectral image usually appears in groups

the intra-band correlation is strong

and the inter-band correlation is weak. Thus

band selection is used for dimensionality reduction

and band grouping is utilized to feed features to multi-classifiers. Specifically

the hyperspectral data are grouped according to the correlation coefficient matrix. Then

spatial-spectral features are extracted for the bands with strong correlation in each group. Gabor and local binary pattern (LBP) features are proven to be suitable and powerful for hypershectral image(HSI) classification. The former is oriented to global features

and the latter is oriented to local features. These features are investigated for the proposed multi-classifier system. Apart from principal component analysis

two advanced local protection dimensionality reduction methods

namely

locality-preserving nonnegative matrix factorization (LPNMF) and locality fisher discriminant analysis (LFDA)

are also used in the proposed system. LPNMF aims to find a low-dimensional subspace and use its single element to represent the categories of ground objects for effectively protecting the diverse local structures of the original hyperspectral image. LFDA is suitable for dimensionality reduction of multi-model data

which can protect the local information between adjacent pixels by linear spectrum. Next

Gaussian mixture model (GMM) classifiers or support vector machine (SVM) classifiers are used to classify each group of features. Finally

the adjustMV decision fusion strategy and adjustLOGP decision fusion strategy are designed

which are based on the weight coefficient optimization allocation for sub-classifiers. They minimize the effect of band groups and abnormal values with low classification accuracy on the final classification results.

Result

2

Multiple features and two classifiers are executed crosswise for classification to verify the effectiveness of the proposed decision fusion strategies and multi-classifier system. Experiments are conducted on two public hyperspectral datasets

namely

the Indian Pines and Pavia University datasets. Experimental results show that the classification accuracies of the adjustMV strategy for Indian Pines and Pavia University datasets are improved by 1.02% and 3.39%

respectively

when Gabor features and GMM classifiers are used for MV decision fusion. When LBP features and SVM classifiers are used for MV decision fusion

the accuracies of the adjustMV strategy for Indian Pines and Pavia University datasets are improved by 1.32% and 0.82%

respectively. GMM classifiers followed by LFDA

LPNMF

or Gabor features

as three basic lines

are conducted into adjustLOGP strategy. For Indian Pines dataset

three adjustLOGP-based algorithms drive higher classification accuracies (18.54%

5.14%

and 2.87%) than three LOGP-based algorithms. For Pavia University dataset

three adjustLOGP-based algorithms drive higher classification accuracies (2.06%

5.81%

and 6.99%) than three LOGP based algorithms. Experiments are also conducted on the two datasets with different numbers of training samples and different powers of noise. The proposed adaptive decision fusion strategies still have better classification performance

even in situations of small sample size and environments with noise. It also has better stability by computing the standard deviations from 20 trails. When the number of training samples is 70

the classification accuracies of the two proposed decision fusion strategies on the Indian Pines and Pavia University datasets have 1.97% and 1.03% improvements compared with the unified weight fusion strategies. When the number of training samples is 30

the classification accuracies of the two proposed decision fusion strategies on the Indian Pines and Pavia University datasets have 4.91% and 3.55% improvements. Gaussian noise is added to the two hyperspectral datasets to further study the robustness of the improved strategies. The classification accuracies of 10 decision fusion algorithms are compared in the case that the signal-to-noise ratio(SNR) of noised datasets is 20 dB. Clearly

five adjustMV-based and adjustLOGP-based strategies are superior to the MV-based and LOGP-based strategies in the noise environment.

Conclusion

2

Two adaptive decision fusion strategies and a multi-classifier system are proposed for hyperspectral image classification in this study. The proposed decision fusion strategies consider allocating different weights with higher reliability to sub-classifiers according to their classification performance. The proposed adjustMV can cast a more important vote to obtain a final decision

and the proposed adjustLOGP has a higher probability of a posteriori to increase the probability of a correct decision. The proposed strategies have low complexity

but they can greatly improve the classification accuracy and possess better stability

even under situations of small sample size and environments with noise.