Fusion algorithm evaluation of Landsat 8 panchromatic and multispetral images in arid regions

doi:10.6046/gtzyyg.2019.04.02

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Abstract

With lower contrast and confidence level, single factor evaluation index is not very effective in the comprehensive evaluation of pixel level image fusion algorithms of Landsat 8 in arid regions. Based on the Landsat 8 image of Juyanze area, 11 single factor indicators and object-oriented classification method were used to compare the following six image fusion algorithms, i.e., Principal Component (PC), Brovey Transform (BT), Hue-Saturation-Value Transform (HSV), Gram-Schmidt Pan Sharpening (G-S), High-pass filtering(HPF) and Wavelet Transform (WT) according to the spatial information quantity, spectral feature and classification accuracy. The results indicate that the spatial resolution and texture features of all fusion images are enhanced in comparison with the original image. HSV is proved to be the best algorithm to highlight the texture features in arid regions, but its spectral fidelity is bad. WT exhibits an excellent capability in maintaining the spectral information, and its capability of revealing spatial details is just next to the HSV method. Therefore, WT is considered the most suitable algorithm for image fusion of Landsat 8 in this study. Taking the spatial information quantity and spectral features into account simultaneously, the authors hold that PC and G-S have moderate performance, and their performance is a little lower than that of HPF, while the performance of BT is the worst. The classification results show that the classification accuracy of WT and HPF is improved to some extent compared with the original image.

Keywords fusion algorithm spectral information spatial information object-oriented classification effect evaluation

TP79

Issue Date: 03 December 2019

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	Liping YANG
	Meng MA
	Wei XIE
	Xueping PAN

Cite this article:

Liping YANG,Meng MA,Wei XIE, et al. Fusion algorithm evaluation of Landsat 8 panchromatic and multispetral images in arid regions[J]. Remote Sensing for Land & Resources, 2019, 31(4): 11-19.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.04.02 OR https://www.gtzyyg.com/EN/Y2019/V31/I4/11

Fig.1 Location of research area

评价指标	计算公式		参数及含义
均值(U),表示影像像素灰度的平均值	$U = 1 MN ∑ i = 1 M ∑ j = 1 N Z i, j$		i和j分别为同一波段影像中各像元的行列号; M和N分别为影像行列数; Z(i,j)为像元的灰度值
标准差( $σ$ ),反映图像各像元灰度离散情况	$σ = 1 MN ∑ i = 1 M ∑ j = 1 N Z (i, j) - Z - 2$		$Z -$ 为像元的灰度平均值
平均梯度( $G -$ ),反映图像对微小细节变化的表达能力	$G - = 1 M × N ∑ i = 1 M ∑ j = 1 N 12 Δ I x 2 + Δ I y 2$		$Δ I x$ 和 $Δ I y$ 分别表示x和y方向的一阶差分
信息熵(H),表示偏离影像直方图高峰灰度区的大小	$H = - ∑ i = 1 L P i lb P i$		L为图像的最大灰度级; P_i为图像像元灰度值为i的概率
联合熵(CE),用于评价多波段影像总信息量	$CE = - ∑ i 1 = 0 L - 1 ∑ i 2 = 0 L - 1 P i 1 i 2 lb P i 1 i 2$		$P i 1 i 2$ 表示图像X和Y的像元灰度值i₁和i₂的联合分布概率
空间频率(SF),反映影像的总体活跃程度,可通过行频率和列频率计算	$SF = H F 2 + V F 2 HF = ∑ i = 1 M ∑ j = 2 N Z i, j - Z i, j - 1 2 M N - 1 VF = ∑ i = 2 M ∑ j = 1 N Z i, j - Z i - 1, j 2 M - 1 N$		HF为水平方向频率; VF为垂直方向频率
结构相似性(SSIM),是符合人眼视觉系统特性的图像质量客观评价指标	$SSIM X, Y = L X, Y × C X, Y × S X, Y = 2 u X u Y + C 1 2 σ XY + C 2 u X 2 + u Y 2 + C 1 σ X 2 + σ Y 2 + C 2 L X, Y = 2 u X u Y + C 1 u X 2 + u Y 2 + C 1 C X, Y = 2 σ X σ Y + C 2 σ X 2 + σ Y 2 + C 2 S X, Y = σ XY + C 3 σ X σ Y + C 3$		L(X,Y),C(X,Y)和S(X,Y) 分别为亮度比较、对比度比较和结构比较; $u X$ , $u Y$ , $σ X 2$ , $σ Y 2$ 和 $σ XY$ 分别为影像X和Y的均值、方差和协方差; C₁,C₂和C₃为常数; C₁=(K₁×L)²; C₂=(K₂×L)²; C₃=C₂/2; K₁=0.01; K₂=0.03; L=255
峰值信噪比(PSNR),衡量图像失真或噪声水平的客观指标	$PSNR = 10 lg MN × Max Y i, j 2 ∑ i = 1 M ∑ j = 1 N X i, j - Y i, j 2$		X(i,j)为原始多光谱影像像素值; Y(i,j)为融合影像像素值; Max[Y(i,j)]为融合影像最大值
光谱扭曲度(D_i),用于评价多光谱信息的保持程度	$D i = 1 MN ∑ i = 1 M ∑ j = 1 N X i, j - Y i, j$
评价指标		计算公式	参数及含义
偏差指数(D),表示融合影像和原始影像灰度值的偏差		$D = 1 MN ∑ i = 1 M ∑ j = 1 N X i, j - Y i, j Y i, j$
相关系数(g),反映2幅影像的相关程度		$γ = ∑ i = 1 M ∑ j = 1 N X i, j - u X Y i, j - u Y ∑ i = 1 M ∑ j = 1 N X i, j - u X 2 Y i, j - u Y 2$

Tab.1 Evaluation index

Fig.2 Comparison of image fusion results

Fig.3 Comparison of image fusion results in local areas A, B and C

Tab.2 Evaluation indicators of fusion results

Fig.4 Classification results of different fusion methods

Tab.3 Accuracy evaluation

Fig.5 Accuracy comparison of different ground objects

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