Hyperspectral Image analysis has gained much attention due to the presence of rich spectral information. Hyperspectral Image (HSI) classification is being utilized in a wide range of applications. Convolutional Neural Networks (CNN) are popularly used in the image classification tasks due to their capability of extracting spatial features from the raw image data. Creating an ensemble of multiple classifiers generates more robust and reliable classification results. In this paper, we propose an ensemble of four CNN classifiers with superpixel smoothing for the task of HSI classification. Stacked Auto-encoder is utilized to reduce the dimensionality of the hyperspectral data. A new method is suggested to derive the optimal number of features by exploiting the diversity among the classifiers. The uniform Local Binary Patterns (ULBP) are extracted from the HSI and is used along with reduced HSI data for classification. The two single-channel models take reduced HSI cubes as input. The two dual-Channel CNN models explore both ULBP patterns and HSI data simultaneously. We explore various techniques for combining the predictions of individual classifiers and choose the best one for ensembling purpose. The obtained prediction map is made to undergo superpixel based smoothing to remove most of the misclassified pixels. Experimental results on standard data sets confirm the superiority of the proposed ensemble model over the state of the art models. The advantages of superpixel smoothing after CNN classifications are also validated through numerical results and corresponding classification maps.

高光谱图像分析由于存在丰富的光谱信息而备受关注。高光谱图像（HSI）分类已被广泛应用。卷积神经网络（CNN）由于能够从原始图像数据中提取空间特征，因此在图像分类任务中被广泛使用。创建多个分类器的集合可生成更强大和可靠的分类结果。在本文中，我们提出了四个具有超像素平滑功能的CNN分类器的集合，用于HSI分类的任务。堆叠式自动编码器用于减少高光谱数据的维数。提出了一种通过利用分类器之间的多样性来推导最佳特征数量的新方法。从HSI中提取统一的本地二进制模式（ULBP），并将其与简化后的HSI数据一起用于分类。这两个单通道模型采用减少的HSI立方体作为输入。两个双通道CNN模型同时探索ULBP模式和HSI数据。我们探索各种技术来组合各个分类器的预测，并为组合目的选择最佳的分类器。使所获得的预测图经受基于超像素的平滑以去除大部分错误分类的像素。在标准数据集上的实验结果证实了提出的集成模型优于现有模型的优越性。CNN分类后超像素平滑的优势也通过数值结果和相应的分类图得到了验证。