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

F Poorahangaryan,H Ghassemian,Li-Tao Zhang

doi:10.1155/2020/8884965

Abstract

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.

Highlights

Hyperspectral imagery (HIS) records reflectance values of the electromagnetic spectra in more than hundred spectral bands for each spatial position in the image
We present a new index called the homogeneity order to extract the pixels. en the results are considered as the input to Knearest neighbor (KNN) that searches in feature space. e obtained pixels are regarded as markers for the Minimum Spanning Forest (MSF) algorithm, and the spatial-spectral classification map is produced
Our method is named as HKNN-MSF-MV. e results of our method are given

Summary

Introduction

Hyperspectral imagery (HIS) records reflectance values of the electromagnetic spectra in more than hundred spectral bands for each spatial position in the image. The results of the experiments of two mentioned studies indicate that the marker-controlled segmentation has good performance in hyperspectral image classification, the markers were selected based on the performance of SVM classifiers. If another pixel-wise classifier is used, it may lead to different markers. The spectral-spatial classifier is proposed based on algebraic multigrid (AMG) method and hierarchical segmentation (HSEG) algorithm [12] In this scheme, the AMG method is performed on the hyperspectral image, and a multigrid structure is generated. A spectral-spatial hyperspectral image classification method based on MSF is presented, which used a new strategy for the selection of markers.

Methodology

Proposed Methods

Conclusion