基于光谱与空间特征结合的改进高光谱数据分类算法

针对仅利用光谱信息进行分类未充分利用高光谱数据图谱合一特性的问题，提出了基于马尔可夫随机场的改进分类模型，利用基于最大后验概率的马尔科夫随机场模型进行光谱与空间信息的融合应用，采用基于光谱信息的概率支持向量机方法提高马尔科夫随机场模型中光谱能量函数项的类条件概率估计精度，设计基于信息传播策略、信息更新策略、多尺度传播策略的多重加速策略的高效置信传播优化算法，解决了马尔科夫随机场模型中全局能量最小化优化过程中计算复杂度高、计算耗时等问题。利用航空可见-近红外成像光谱仪AVIRIS对美国印第安纳州西北部的农业示范区数据进行应用分析，并与迭代条件模型、模拟退火、置信传播等方法进行性能比较，试验结果表明：该方法能够达到总体分类精度95.78%、Kappa系数0.933 4，优于现有马尔科夫随机场分类算法，并且计算效率比置信传播优化算法提高了3倍以上。

An Improved Classification Approach Based on Spatial and Spectral Features for Hyperspectral Data

The spatial correlativity and spectral information are not applied synchronously in the classification model of hyperspectral data. To solve this problem, an improved classification approach based on Markov random field (MRF) theory is proposed in our work. The MRF model based on maximum a posteriori is applied to combine the spectral and spatial information. The probabilistic support vector machine (PSVM) algorithm using pixels spectral information is applied to improve the estimation accuracy of the class conditional probability (CCP) of the spectral energy function, and the efficient belief propagation (EBP) based on multi-accelerated strategy (such as ordinal propagated message strategy, linearized message-updating strategy, and coarse-to-fine approach) is developed in order to solve the problem of the high calculational complexity and time-consumed in the global energy minimum optimization of MRF model. The true hyperspectral data collected by airborne visible infrared imaging spectrometer (AVIRIS) is applied to estimate the performance of the proposed approach in the agricultural demonstration area, Indiana northwest, USA. The performance of the proposed approach is compared with simulated annealing and iterated conditional model. The results illuminate that the average classification accuracy of our method reachs to 95.78%, and the Kappa coefficient is 93.34%, much higher than that of the result by the traditional MRF classification algorithms, and the computational efficiency is improved more than 3 times compared with the belief propagation algorithm.