论文标题
高光谱图像的形态分割
Morphological segmentation of hyperspectral images
论文作者
论文摘要
本文为高光谱图像的形态分割（即具有重要的通道数量）开发了一种通用方法。这种基于流域的方法由光谱分类组成，以获取标记和提供空间信息的矢量梯度。几种替代梯度适合不同的高光谱功能。减少数据是通过因子分析或模型拟合进行的。图像分割是在不同空间上进行的：因子空间，参数空间等。在所有这些空间上，都应用了空间/光谱分割方法，从而在图像上取得了相关结果。
The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on watershed, is composed of a spectral classification to obtain the markers and a vectorial gradient which gives the spatial information. Several alternative gradients are adapted to the different hyperspectral functions. Data reduction is performed either by Factor Analysis or by model fitting. Image segmentation is done on different spaces: factor space, parameters space, etc. On all these spaces the spatial/spectral segmentation approach is applied, leading to relevant results on the image.
