针对用PCA融合方法进行高光谱遥感影像和高分影像融合会出现一定程度的光谱失真问题, 提出了一种改进的弱光谱畸变PCA融合方法。 采用NCUT（normalized cut）影像分割算法, 将复杂的高光谱遥感影像对象化, 增加融合样本的线性可分性, 从而削弱传统PCA融合产生的光谱畸变; 运用图论和聚类理论生成表达像素间相似度的权重矩阵和若干掩膜, 并用这些掩膜切割高光谱影像与高分影像, 再分别融合其对应匹配的子区域对象, 最后将所有子区域融合结果拼接成一幅影像。 使用Hyperion高光谱数据和Rapid Eye高分影像进行实验, 结果表明: 该方法在保证融合结果空间分辨率提升和纹理信息不变的前提下, 光谱保真能力优于传统的PCA融合方法。

Aiming at the spectral distortion produced in PCA fusion process, the present paper proposes an improved low spectral distortion PCA fusion method. This method uses NCUT(normalized cut)image segmentation algorithm to make a complex hyperspectral remote sensing image into multiple sub-images for increasing the separability of samples, which can weaken the spectral distortions of traditional PCA fusion; Pixels similarity weighting matrix and masks were produced by using graph theory and clustering theory. These masks are used to cut the hyperspectral image and high-resolution image into some sub-region objects. All corresponding sub-region objects between the hyperspectral image and high-resolution image are fused by using PCA method, and all sub-regional integration results are spliced together to produce a new image. In the experiment, Hyperion hyperspectral data and Rapid Eye data were used. And the experiment result shows that the proposed method has the same ability to enhance spatial resolution and greater ability to improve spectral fidelity performance.