Abstract The contiguous acquisition of information in narrow wavelength in hyperspectral images poses complex problems in processing of the bands at various stages. The complexity arises due to the high dimensionality and the redundancy of information can easily be addressed with deep networks. In this research work, initially spatio-spectral features are fused by extracting the uncorrelated bands and exploit the texture patterns via exploratory factor analysis and Gabor filter respectively and embedded these features to the original cube underlying the assumption that the noise is heteroscedastic in each of the variable in factor analysis. Later, from the resultant Gabor embedded hyperspectral cube, extracted different number of patch cubes of sizes 25 × 25 × bands and trained an evolving newly designed deep network, three dimensional convolution neural networks, to classify the labels of hyperspectral cube. Experiments are conducted on the three bench mark datasets, namely, Indian Pines, University of Pavia and Salinas. The proposed method exhibits with high accuracy in performance over the state of the art methods as the convolution neural network is trained with Gabor embedded patches. The Overall Accuracy of the proposed method is 99.69%, 99.85% and 99.65% for Indian Pines, University of Pavia and Salinas dataset respectively.

中文翻译：

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一种使用基于 Gabor 嵌入补丁的卷积神经网络的高光谱图像分类新框架

摘要 高光谱图像窄波长信息的连续获取给各阶段波段的处理带来了复杂的问题。由于高维数和信息冗余可以很容易地通过深度网络解决，因此复杂性增加。在这项研究工作中，最初通过提取不相关的波段来融合空间光谱特征，并分别通过探索性因子分析和 Gabor 滤波器利用纹理模式，并将这些特征嵌入到原始立方体中，假设噪声在每个因子分析中的变量。后来，从生成的 Gabor 嵌入高光谱立方体中，提取不同数量的大小为 25 × 25 × 波段的补丁立方体，并训练一个不断发展的新设计的深度网络，三维卷积神经网络，对高光谱立方体的标签进行分类。实验在三个基准数据集上进行，即印度松树、帕维亚大学和萨利纳斯。由于卷积神经网络是用 Gabor 嵌入补丁训练的，因此所提出的方法在性能方面表现出比现有技术方法高的精度。对于印度松树、帕维亚大学和萨利纳斯数据集，所提出方法的总体准确率分别为 99.69%、99.85% 和 99.65%。由于卷积神经网络是用 Gabor 嵌入补丁训练的，因此所提出的方法在性能方面表现出比现有技术方法高的精度。对于印度松树、帕维亚大学和萨利纳斯数据集，所提出方法的总体准确率分别为 99.69%、99.85% 和 99.65%。由于卷积神经网络是用 Gabor 嵌入补丁训练的，因此所提出的方法在性能方面表现出比现有技术方法高的精度。对于印度松树、帕维亚大学和萨利纳斯数据集，所提出方法的总体准确率分别为 99.69%、99.85% 和 99.65%。