Hyperspectral image rolling guidance recursive filtering and classification

Binge CUI; Yanan WU; Yong ZHONG; Liwei ZHONG; Yan LU

doi:10.11834/jrs.20197510

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Hyperspectral image rolling guidance recursive filtering and classification
Vol. 23, Issue 3, Pages: 431-442(2019)
Received：30 November 2017，

Accepted：17 April 2018，

Published：2019-05
DOI： 10.11834/jrs.20197510
稿件说明：

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崔宾阁, 吴亚男, 钟勇, 钟利伟, 路燕. 2019. 高光谱图像滚动引导递归滤波与地物分类. 遥感学报, 23(3): 431–442 DOI： 10.11834/jrs.20197510.

Cui B G, Wu Y N, Zhong Y, Zhong L W and Lu Y. 2019. Hyperspectral image rolling guidance recursive filtering and classification. Journal of Remote Sensing, 23(3): 431–442 DOI： 10.11834/jrs.20197510.

摘要

高光谱图像类内光谱变化较大，“同物异谱”现象普遍存在。利用原始地物光谱特征进行分类精度较低而且分类结果图中存在“椒盐现象”。为了获得好的分类结果，必须充分利用高光谱图像的光谱信息和空间信息，减少类内的光谱变化，并扩大类别间的光谱差异。为此，提出一种滚动引导递归滤波的高光谱图像光谱—空间分类方法。首先，利用主成分分析对高光谱图像进行降维；然后，利用高斯滤波对输入图像进行模糊化，消除图像中的噪声和小尺度结构；接下来，将模糊化后的图像作为引导图像，对输入图像进行边缘保持递归滤波，输出结果作为新的引导图像，重复迭代这个过程直至大尺度边缘被恢复；最后，利用提取的特征波段和支持向量机对高光谱图像进行分类。在两个真实高光谱数据集上进行了分类实验，结果表明本文方法的分类精度优于其他的高光谱图像分类方法。在训练样本极少的情况下，本文方法也能获得较高的分类精度。

Abstract

Hyperspectral images have relatively large changes in intra-class spectra

and the “same material

different spectra” phenomena are widespread. Moreover

classification accuracy is low when only the original spectral features are considered

and the “pepper and salt phenomenon” can be observed in the classification result map. In the attempt to improve the classification result

the spectral and spatial information of a hyperspectral image needs to be fully utilized to reduce the spectral changes within the class and expand the spectral difference between classes. Thus

in this study

we propose a spectral–spatial hyperspectral image classification method with rolling guidance recursive filtering. First

principal component analysis (PCA) is used to reduce the dimension of the hyperspectral image

in which the main components with large amounts of information are maintained for the input image of the next step. Second

the Gaussian filter is adopted to blur the input image and eliminate the small-scale structure. Third

recursive filtering is performed on the input image to preserve the edge of the image

in which the guidance image is the blurred image

and then the output result is treated as the new guidance image. The process is repeated iteratively until the large-scale edge is restored. Finally

the extracted feature bands of the hyperspectral image are used for classification by a support vector machine. Indian Pines and University of Pavia datasets were initially used to verify the effectiveness of the proposed rolling guidance recursive filtering (RGRF) method. As shown by the homogeneous regions in

Figs. 5

and

the noise phenomenon is severe when the extracting feature of PCA is used

whereas the noise is effectively removed when rolling guidance filtering (RGF) and RGRF are utilized. However

at the strong edges

the boundary is blurred and irregular and the spatial smoothness is excessive for RGF. By contrast

the boundary is clear and the strong edges are protected for RGRF. Then

the proposed method is compared with related classification methods by using two real hyperspectral image datasets. The comparative results in

Tables 1

and

indicate that RGRF can improve classification accuracy more effectively than the other methods. Subsequently

McMemar test was performed to analyze the statistical significance and verify the comparative results. As shown in

Tables 3

and

the correlations of RGRF with the other methods are statistically significant

and the values exceed

1.96. The McMemar values in

Tables 3

and

increase with the decrease in number of samples

which indicates that the advantages of using RGRF becomes more obvious in the case of fewer samples. This study presents an improved edge-preserving filtering method named RGRF. Compared with the other filtering methods

RGRF can maintain the edge structures of land covers and eliminate the texture and noise information within these land covers. RGRF effectively uses the recursive filtering method instead of the bilateral filtering method (i.e.

of the RGF method) in each iteration. In this manner

the continuous spreading of texture and noise information across the strong edges is avoided. Moreover

considering that image quality is significantly improved after RGRF filtering

the classification accuracy of the hyperspectral image is also greatly enhanced. Classification experiments were performed on two real hyperspectral datasets. The results indicate that the classification accuracy of RGRF is superior to the other hyperspectral image classification methods. Moreover

RGRF can achieve higher classification accuracy when only few training samples are used.

关键词

Keywords

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