In recent years, Gabor filtering has been successfully applied in spectral–spatial hyperspectral image (HSI) classification tasks due to its strong power to characterize surface materials. Generally, the standard Gabor filter involves the real and imaginary parts. However, the traditional usage to integrate both the two parts, i.e., the Gabor magnitude feature, might weaken some unique characteristics of each part, therefore leading to possible information loss. To solve this problem, we proposed a flexible 3-D Gabor features fusion (F3DGF) approach to make better use of two parts of Gabor features, based on the phase-induced 3-D Gabor feature, which is rarely exploited before. As the term suggests, the phase-induced feature is guided by a phase parameter P, which can flexibly combine the information from two parts of Gabor components. The proposed F3DGF scheme explores all the phase-induced 3-D Gabor features by means of a decision-level fusion strategy, where the obtained probability outputs are directly gathered to generate the decision map. Experimental results on three real HSIs demonstrate that our approach exhibits good improvements, as compared to the traditional real part and the magnitude Gabor features-based classification methods. The results show great potential to introduce phase-induced 3-D Gabor features for classification tasks.

中文翻译：

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用于高光谱图像分类的灵活 3-D Gabor 特征融合

近年来，Gabor 滤波由于其强大的表征表面材料的能力，已成功应用于光谱空间高光谱图像 (HSI) 分类任务。通常，标准 Gabor 滤波器涉及实部和虚部。然而，传统的将这两个部分集成的用法，即Gabor幅度特征，可能会削弱每个部分的一些独特特征，从而导致可能的信息丢失。为了解决这个问题，我们提出了一种灵活的 3-D Gabor 特征融合 (F3DGF) 方法，以更好地利用 Gabor 特征的两部分，基于相位诱导的 3-D Gabor 特征，这在以前很少被利用。顾名思义，相位诱导特征由相位参数 P 引导，它可以灵活地组合来自 Gabor 分量的两个部分的信息。所提出的 F3DGF 方案通过决策级融合策略探索所有相位诱导的 3-D Gabor 特征，其中直接收集获得的概率输出以生成决策图。在三个真实 HSI 上的实验结果表明，与传统的实部和基于 Gabor 特征的量级分类方法相比，我们的方法表现出良好的改进。结果显示了为分类任务引入相位诱导 3-D Gabor 特征的巨大潜力。与传统的实部和幅度 Gabor 基于特征的分类方法相比。结果显示了为分类任务引入相位诱导 3-D Gabor 特征的巨大潜力。与传统的实部和幅度 Gabor 基于特征的分类方法相比。结果显示了为分类任务引入相位诱导 3-D Gabor 特征的巨大潜力。