Convolutional neural networks (CNNs) can automatically learn features from the hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features. However, the number of training samples for the classification of HSIs is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. To solve this problem, a pixel cluster CNN and spectral-spatial fusion (SSF) algorithm for hyperspectral image classification with small-size training samples is proposed in this article. First, spatial information is extracted by the gray level co-occurrence matrix. Then, spatial information and spectral information are fused by means of bands superposition, forming spectral-spatial features. To expand the number of training samples, the pixels after SSF are combined into pixel clusters according to a certain rule. Finally, a CNN framework is utilized to extract effective features from the pixel clusters. Experiments based on three standard HSIs demonstrate that the proposed algorithm can get better performance than the conventional CNN and also outperforms other studied algorithms in the case of small training set.

中文翻译：

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小样本训练样本的高光谱图像分类的像素簇CNN和光谱空间融合算法

卷积神经网络（CNN）可以自动从高光谱图像（HSI）数据中学习特征，从而避免了手动提取特征的困难。但是，用于HSI分类的训练样本数量总是有限的，这使得CNN难以获得有效的特征，并且导致分类精度低。为解决这一问题，本文提出了一种具有小样本训练样本的高分辨图像分类的像素簇CNN和光谱空间融合（SSF）算法。首先，通过灰度共生矩阵提取空间信息。然后，通过频带叠加将空间信息和光谱信息融合，形成光谱空间特征。为了增加训练样本的数量，SSF之后的像素按照一定的规则组合成像素簇。最后，利用CNN框架从像素簇中提取有效特征。基于三个标准HSI的实验表明，所提出的算法可以比常规的CNN获得更好的性能，并且在训练量较小的情况下，其性能也优于其他研究的算法。