等距映射和局部切空间排列降维后，低维流形坐标能够保留原始高光谱影像中地物光谱信息，用于提取原始影像的潜在特征。然而这两种流形方法的理论差异导致其低维坐标继承光谱信息的能力不同，对比这两种流形坐标可凸显出原始影像内部的潜在特征。因此，本文基于等距映射和局部切空间排列非线性降维，提出两种流形坐标的差异图法来提取高光谱影像内部的潜在特征。首先，根据流形坐标的光谱解释确定两种坐标的每一维代表相同的光谱信息。其次，根据相同的光谱特征，归一化两种流形坐标并调整坐标轴方向，统一两种坐标到相同的坐标框架。最后，通过加权流形图相减得到坐标差异图，采用经典的图像处理方法提取潜在特征。采用两个实验并对比等距映射和局部切空间排列方法的降维结果来验证本文方法。结果表明，流形坐标差异图能够成功提取单一流形结果无法得到的潜在特征，如靠河岸的浅水区域和大场景沼泽地中的低分辨率道路。这为高光谱影像的潜在特征提取研究提供了一种新方法。

Manifold coordinates from Isometric mapping (Isomap) and Local Tangent Space Alignment (LTSA) preserve the spectral features of ground objects from Hyperspectral Imagery (HSI) through nonlinear dimensionality reduction. However, the theoretical differences result in differing capabilities in preserving spectral features. Thus, a comparison of two coordinates can make the underlying features prominent. Therefore, this paper proposes an innovative method called Difference Maps from Manifold Coordinates (DMMC), which is based on Isomap and LTSA, to extract underlying features. First, spectral interpretations are matched with both coordinates and ensured to preserve the same spectral features. Second, the Isomap and LTSA coordinates are transformed into a uniform system using coordinate normalization and axis-direction adjustment. Finally, the difference maps are obtained through subtraction operations between the weighted manifold maps, and underlying features are extracted using classical image processing approaches. Two case studies are performed to evaluate the proposed method, and the results are compared with those obtained using Isomap and LTSA. The results show that DMMC outperforms Isomap and LTSA in extracting underlying features, such as the underlying shallow water near the river bank and the low spatial-resolution road in the large image scene of a swamp. This method provides a novel scheme for extracting underlying features from HSI data.