目的 目前高光谱图像决策融合方法主要采用以多数票决（majority vote，MV）为代表的硬决策融合和以对数意见池（logarithmic opinion pool，LOGP）为代表的软决策融合策略。由于这些方法均使用统一的权重系数进行决策融合，没有对子分类器各自的分类性能进行评估而优化分配权重系数，势必会影响最终的分类精度。针对该问题，本文对多数票决和对数意见池融合策略进行了改进，提出了面向高光谱图像分类的自适应决策融合方法。方法 根据相关系数矩阵对高光谱图像进行波段分组，对每组波段进行空谱联合特征提取；利用高斯混合模型（Gaussian mixture model，GMM）或支持向量机（support vector machine，SVM）分类器对各组空谱联合特征进行分类；最后，采用本文研究的两种基于权重系数优化分配的自适应融合策略对子分类器的分类结果进行决策融合，使得分类精度低的波段组和异常值对最终分类结果的影响达到最小。结果 对两个公开的高光谱数据集分别采用多种特征和两种分类器组合进行实验验证。实验结果表明，在相同特征和分类器条件下，本文提出的自适应多数票决策融合策略（adjust majority vote，adjustMV）、自适应对数意见池决策融合策略（adjust logarithmic opinion pool，adjustLOGP）比传统的MV决策融合策略、LOGP决策融合策略对两个数据集的分类精度均有大幅度提高。Indian Pines数据集上，adjustMV算法的分类精度比相应的MV算法平均提高了1.2%，adjustLOGP算法的分类精度比相应的LOGP算法平均提高了7.38%；Pavia University数据集上，adjustMV算法的分类精度比相应的MV算法平均提高了2.1%，adjustLOGP算法的分类精度比相应的LOGP算法平均提高了4.5%。结论 本文提出的自适应权重决策融合策略为性能较优的子分类器（即对应于分类精度高的波段组）赋予较大的权重，降低了性能较差的子分类器与噪声波段对决策融合结果的影响，从而大幅度提高分类精度。所研究的决策融合策略的复杂度和计算成本均较低，在噪声环境中具有更强的鲁棒性，同时在一定程度上解决了高光谱图像分类应用中普遍存在的小样本问题。

Objective 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 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 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 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.