Spectral-Spatial Hyperspectral Image Classification Based on Homogeneous Minimum Spanning Forest

被引:1
|
作者
Poorahangaryan, F. [1 ]
Ghassemian, H. [2 ]
机构
[1] Ayandegan Inst Higher Educ, Dept Elect Engn, Tonekabon, Iran
[2] Tarbiat Modares Univ, Fac Elect & Comp Engn, Image Proc & Informat Anal Lab, Tehran, Iran
关键词
SEGMENTATION;
D O I
10.1155/2020/8884965
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The combination of spectral and spatial information is known as a suitable way to improve the accuracy of hyperspectral image classification. In this paper, we propose a spectral-spatial hyperspectral image classification approach composed of the following stages. Initially, the support vector machine (SVM) is applied to obtain the initial classification map. Then, we present a new index called the homogeneity order and, using that with K-nearest neighbors, we select some pixels in feature space. The extracted pixels are considered as markers for Minimum Spanning Forest (MSF) construction. The class assignment to the markers is done using the initial classification map results. In the final stage, MSF is applied to these markers, and a spectral-spatial classification map is obtained. Experiments performed on several real hyperspectral images demonstrate that the classification accuracies obtained by the proposed scheme are improved when compared to MSF-based spectral-spatial classification approaches.
引用
收藏
页数:11
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