Spectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
Spectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
复制标题
具有边缘保留滤波的光谱空间高光谱图像分类
DOI：
10.1109/tgrs.2013.2264508
复制
发表时间：
2014-05-01
影响因子：
8.2
通讯作者：
Benediktsson, Jon Atli
中科院分区：
文献类型：
--
作者：
Kang, Xudong;Li, Shutao;Benediktsson, Jon Atli
在高光谱图像分类中结合空间背景信息是提高分类精度的有效途径。提出了一种基于边缘保持滤波的谱-空分类框架。拟议框架包括以下三个步骤。首先，使用逐像素分类器对高光谱图像进行分类。例如，在一个实施例中，支持向量机分类器然后，将得到的分类图表示为多个概率图，并对每个概率图进行边缘保持滤波，其中高光谱图像的第一主成分或前三个主成分用作灰度或彩色引导图像。最后，根据滤波后的概率图，基于最大概率选择每个像素的类别。实验结果表明，基于边缘保持滤波的分类方法可以在很短的时间内显著提高分类精度。因此，它可以很容易地应用于真实的应用中。
The integration of spatial context in the classification of hyperspectral images is known to be an effective way in improving classification accuracy. In this paper, a novel spectral-spatial classification framework based on edge-preserving filtering is proposed. The proposed framework consists of the following three steps. First, the hyperspectral image is classified using a pixelwise classifier, e. g., the support vector machine classifier. Then, the resulting classification map is represented as multiple probability maps, and edge-preserving filtering is conducted on each probability map, with the first principal component or the first three principal components of the hyperspectral image serving as the gray or color guidance image. Finally, according to the filtered probability maps, the class of each pixel is selected based on the maximum probability. Experimental results demonstrate that the proposed edge-preserving filtering based classification method can improve the classification accuracy significantly in a very short time. Thus, it can be easily applied in real applications.