School of Computer Science, China University of Geosciences, Wuhan 430074, China.
Beibu Gulf Big Data Resources Utilisation Lab, Qinzhou University, Qinzhou 535000, China.
Sensors (Basel). 2018 Jun 20;18(6):1978. doi: 10.3390/s18061978.
Recently, image-filtering based hyperspectral image (HSI) feature extraction has been widely studied. However, due to limited spatial resolution and feature distribution complexity, the problems of cross-region mixing after filtering and spectral discriminative reduction still remain. To address these issues, this paper proposes a spectral-spatial propagation filter (PF) based HSI feature extraction method that can effectively address the above problems. The dimensionality/band of an HSI is typically high; therefore, principal component analysis (PCA) is first used to reduce the HSI dimensionality. Then, the principal components of the HSI are filtered with the PF. When cross-region mixture occurs in the image, the filter template reduces the weight assignments of the cross-region mixed pixels to handle the issue of cross-region mixed pixels simply and effectively. To validate the effectiveness of the proposed method, experiments are carried out on three common HSIs using support vector machine (SVM) classifiers with features learned by the PF. The experimental results demonstrate that the proposed method effectively extracts the spectral-spatial features of HSIs and significantly improves the accuracy of HSI classification.
近年来，基于图像滤波的高光谱图像（HSI）特征提取得到了广泛的研究。然而，由于空间分辨率有限和特征分布的复杂性，滤波后仍然存在跨区域混合和光谱可分性降低的问题。针对这些问题，本文提出了一种基于谱-空传播滤波器（PF）的 HSI 特征提取方法，该方法可以有效地解决上述问题。HSI 的维数/带宽通常很高；因此，首先使用主成分分析（PCA）来降低 HSI 的维数。然后，使用 PF 对 HSI 的主成分进行滤波。当图像中发生跨区域混合时，滤波器模板会降低跨区域混合像素的权重分配，从而简单有效地处理跨区域混合像素的问题。为了验证所提出方法的有效性，使用支持向量机（SVM）分类器在三个常见的 HSI 上进行了实验，这些特征是由 PF 学习得到的。实验结果表明，所提出的方法能够有效地提取 HSI 的谱-空特征，显著提高了 HSI 分类的准确性。
