Effective features derived from an original hyperspectral image (HSI) are quite important to improve the classification performance. An improved feature set, namely HGFM, is constructed by integrating harmonic analysis (HA) optimized by a multiscale guided filter (GF) with morphological operation for HSI classification. To establish HGFM, HA is first adopted to convert the HSI from spectral space to the frequency domain represented by amplitude, phase, and residual. With the first component of minimum noise fraction obtained from the original HSI as the guidance image, the harmonic components are then processed by the multiscale GF. Finally, the obtained results are then operated via morphological opening by reconstruction and closing by reconstruction to generate an improved feature set for classification. The HGFM features are input to an ensemble learning (EL) based on classification framework, in which EL plays an auxiliary role to enhance the classification stability and reliability. Three commonly used HSIs are used for experiments, and different feature sets are evaluated by comparing EL and rotation forest, support vector machine optimized by particle swarm optimization, random forest, and others. Compared with benchmark feature sets, the proposed HGFM feature set can better depict the details of objects easily, and the experimental results confirm the effectiveness in terms of classification accuracy and generalization ability.

中文翻译：

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高光谱图像分类的改进特征集：通过多尺度引导滤波器优化的谐波分析


从原始高光谱图像（HSI）导出的有效特征对于提高分类性能非常重要。改进的特征集，即 HGFM，是通过将多尺度引导滤波器（GF）优化的谐波分析（HA）与 HSI 分类的形态学运算相结合而构建的。为了建立HGFM，首先采用HA将HSI从谱空间转换到由幅度、相位和残差表示的频域。以从原始HSI获得的最小噪声分数的第一分量作为引导图像，然后通过多尺度GF处理谐波分量。最后，对获得的结果进行形态学开重构和闭重构操作，生成改进的分类特征集。将HGFM特征输入到基于分类框架的集成学习（EL）中，其中EL起到辅助作用，以增强分类的稳定性和可靠性。使用三种常用的HSI进行实验，通过比较EL和旋转森林、粒子群优化支持向量机、随机森林等来评估不同的特征集。与基准特征集相比，所提出的HGFM特征集可以更好地描述物体的细节，实验结果证实了分类精度和泛化能力方面的有效性。