能量属性与引导滤波结合的遥感影像融合

doi:10.11947/j.AGCS.2023.20220470

测绘学报 ›› 2023, Vol. 52 ›› Issue (12): 2154-2163.doi: 10.11947/j.AGCS.2023.20220470

能量属性与引导滤波结合的遥感影像融合

宋加文, 朱大明, 付志涛, 陈思静

昆明理工大学国土资源工程学院, 云南昆明 650093

收稿日期:2022-07-05 修回日期:2023-06-07 发布日期:2024-01-03
通讯作者: 朱大明 E-mail:11301066@kust.edu.cn
作者简介:宋加文(1997-),男,硕士生,研究方向为遥感影像融合及其信息分析。E-mail:717802899@qq.com
基金资助:
国家自然科学基金(41961053)

Remote sensing image fusion combining energy attribute and guided filter

SONG Jiawen, ZHU Daming, FU Zhitao, CHEN Sijing

Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

Received:2022-07-05 Revised:2023-06-07 Published:2024-01-03
Supported by:
The National Natural Science Foundation of China (No. 41961053)

摘要/Abstract

摘要： 针对多光谱与全色影像融合容易出现空间和光谱信息失真的问题,本文提出一种基于能量属性与引导滤波的全色锐化方法。首先,对多光谱影像进行强度-色调-饱和度变换提取强度分量,强度分量与全色影像通过均值滤波和差分算子得到高频分量和低频分量,其中高频信息通过引导滤波增强;然后,使用像素最大值规则获得决策图;最后,高频图像通过像素加权平均规则与决策图融合。低频分量通过EA策略融合,结合新的低频分量与高频分量替代原强度分量并逆IHS变换得到融合影像。本文在SPOT-6、WorldView-2和Pléiades NEO遥感影像上进行大量试验,并与4种先进方法进行定量定性对比。本文方法的光谱角度映射、相对无量纲全局误差、相对平均光谱误差、均方根误差、通用图像质量指数和峰值信噪比指标值较次优值分别平均提高了77.13％、10.78％、9.57％、12.20％、1.35％及0.39％。试验结果表明,本文方法可以在保持多光谱影像光谱信息的同时充分融入全色影像的空间信息,并在视觉感知、定量指标和时间效率上都取得最优的融合效果。

关键词: 图像融合, 能量属性, 引导滤波, 均值滤波, 全色影像, 多光谱影像

Abstract: A pan-sharpening method based on energy attribute (EA) and guided filter is presented to solve the problem of spatial and spectral information distortion in multispectral and panchromatic image fusion. First, the intensity-hue-saturation (IHS) transformation is applied to the multispectral image to extract the intensity component. The high and low-frequency components are obtained from the intensity component and the panchromatic image using the mean filter and difference operator. The guided filter enhances the high-frequency information. The decision map is obtained using the rule of maximum pixel value, and the high-frequency image is fused with the decision map by the rule of the weighted average pixel. The EA strategy fuses low-frequency components. The fusion image is obtained by combining the new low-frequency component with the high-frequency component instead of the original intensity component and inverting the IHS transformation. In this paper, many experiments are carried out on SPOT-6, WorldView-2, and Pléiades NEO remote sensing images, and the results are compared quantitatively and qualitatively with four advanced methods. The spectral angle mapping, relative dimensionless global error in synthesis, relative average spectral error, root mean square error, universal image quality index, and peak signal-to-noise ratio values of the proposed methods were improved by 77.13%, 10.78%, 9.57%, 12.20%, 1.35%, and 0.39% compared with the sub-optimal values, respectively. The experimental results show that this method can fully incorporate the spatial information of panchromatic images while maintaining the spectral information of multispectral images and achieve optimal fusion results in visual perception, quantitative indicators, and time efficiency.

Key words: image fusion, energy attribute, guided filter, mean filter, panchromatic image, multispe-ctral image

中图分类号:

P237

宋加文, 朱大明, 付志涛, 陈思静. 能量属性与引导滤波结合的遥感影像融合[J]. 测绘学报, 2023, 52(12): 2154-2163.

SONG Jiawen, ZHU Daming, FU Zhitao, CHEN Sijing. Remote sensing image fusion combining energy attribute and guided filter[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(12): 2154-2163.

参考文献

[1] VIVONE G, DALLA MURA M, GARZELLI A, et al. A new benchmark based on recent advances in multispectral pansharpening: revisiting pansharpening with classical and emerging pansharpening methods[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(1): 53-81.
[2] BRUZZONE L, BOVOLO F. A novel framework for the design of change-detection systems for very-high-resolution remote sensing images[J]. Proceedings of the IEEE, 2013, 101(3): 609-630.
[3] PEREIRA O J R, MELFI A J, MONTES C R. Image fusion of Sentinel-2 and CBERS-4 satellites for mapping soil cover in the Wetlands of Pantanal[J]. International Journal of Image and Data Fusion, 2017, 8(2): 148-172.
[4] KUMAR U, MILESI C, NEMANI R R, et al. Multi-sensor multi-resolution image fusion for improved vegetation and urban area classification[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, XL-7/W4: 51-58.
[5] LIU Pengfei, XIAO Liang, LI Tao. A variational pan-sharpening method based on spatial fractional-order geometry and spectral-spatial low-rank priors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(3): 1788-1802.
[6] GUO Penghao, ZHUANG Peixian, GUO Yecai. Bayesian pan-sharpening with multiorder gradient-based deep network constraints[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 950-962.
[7] XIAO Jinliang, HUANG Tingzhu, DENG Liangjian, et al. A new context-aware details injection fidelity with adaptive coefficients estimation for variational pansharpening[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-15.
[8] 王密, 何鲁晓, 程宇峰, 等. 自适应高斯滤波与SFIM模型相结合的全色多光谱影像融合方法[J]. 测绘学报, 2018, 47(1): 82-90. DOI: 10.11947/j.AGCS.2018.20170421. WANG Mi, HE Luxiao, CHENG Yufeng, et al. Panchromatic and multi-spectral fusion method combined with adaptive Gaussian filter and SFIM model[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1): 82-90. DOI: 10.11947/j.AGCS.2018.20170421.
[9] ZHANG Yun. Smart photogrammetric and remote sensing image processing for very high resolution optical images—examples from the CRC-AGIP lab at UNB[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2): 17-26.
[10] JIAN Lihua, YANG Xiaomin, WU Wei, et al. Pansharpening using a guided image filter based on dual-scale detail extraction[J]. Journal of Ambient Intelligence and Humanized Computing, 2018: 1-15.
[11] LI Qilei, YANG Xiaomin, WU Wei, et al. Pansharpening multispectral remote-sensing images with guided filter for monitoring impact of human behavior on environment[J]. Concurrency and Computation: Practice and Experience, 2021, 33(4): e5074.
[12] QIU Xiaohua, LI Min, ZHANG Liqiong, et al. Guided filter-based multi-focus image fusion through focus region detection[J]. Signal Processing: Image Communication, 2019, 72: 35-46.
[13] MA Jinlei, ZHOU Zhiqiang, WANG Bo, et al. Multi-focus image fusion using boosted random walks-based algorithm with two-scale focus maps[J]. Neurocomputing, 2019, 335: 9-20.
[14] HE Kaiming, SUN Jian, TANG Xiaoou. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
[15] TAN Wei, THITØN W, XIANG Pei, et al. Multi-modal brain image fusion based on multi-level edge-preserving filtering[J]. Biomedical Signal Processing and Control, 2021, 64: 102280.
[16] OTAZU X, GONZALEZ-AUDICANA M, FORS O, et al. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(10): 2376-2385.
[17] 成飞飞, 付志涛, 黄亮, 等. 结合自适应PCNN的非下采样剪切波遥感影像融合[J]. 测绘学报, 2021, 50(10): 1380-1389. DOI: 10.11947/j.AGCS.2021.20200589. CHENG Feifei, FU Zhitao, HUANG Liang, et al. Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10): 1380-1389. DOI: 10.11947/j.AGCS.2021.20200589.
[18] LI Xiaosong, ZHOU Fuqiang, TAN Haishu, et al. Multimodal medical image fusion based on joint bilateral filter and local gradient energy[J]. Information Sciences, 2021, 569: 302-325.
[19] YUHAS R H, GOETZ A, BOARDMAN J. Discrimination among semi-arid landscape endmembers using the spectral angle mapper(SAM) algorithm[J]. Summaries of the Third Annual JPL Airborne Geoscience Workshop,1992,1: 147-149.
[20] MENG Xiangchao, XIONG Yiming, SHAO Feng, et al. A large-scale benchmark data set for evaluating pansharpening performance: overview and implementation[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(1): 18-52.
[21] YANG Yong, WAN Weiguo, HUANG Shuying, et al. Remote sensing image fusion based on adaptive IHS and multiscale guided filter[J]. IEEE Access, 2016, 4: 4573-4582.
[22] YANG Yong, TONG Song, HUANG Shuying, et al. Multi-focus image fusion based on NSCT and focused area detection[J]. IEEE Sensors Journal, 2014,15(5):2824-2838.
[23] WANG Z, BOVIK A C. A universal image quality index[J]. IEEE Signal Processing Letters, 2002, 9(3): 81-84.
[24] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

能量属性与引导滤波结合的遥感影像融合

Remote sensing image fusion combining energy attribute and guided filter

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	巩丹超, 韩轶龙, 黄旭. 线特征约束的建筑物密集匹配边缘全局优化方法[J]. 测绘学报, 2021, 50(6): 833-846.
[2]	成飞飞, 付志涛, 黄亮, 陈朋弟, 黄琨. 结合自适应PCNN的非下采样剪切波遥感影像融合[J]. 测绘学报, 2021, 50(10): 1380-1389.
[3]	胡根生, 周文利, 梁栋, 鲍文霞. 融合引导滤波和迁移学习的薄云图像中地物信息恢复算法[J]. 测绘学报, 2018, 47(3): 348-358.
[4]	段伟, 闫利. 融合图像的频率定量分析及其空间分辨率的确定方法[J]. 测绘学报, 2016, 45(6): 691-697.
[5]	朱映, 王密, 潘俊, 胡芬. 利用多光谱影像检测资源三号卫星平台震颤[J]. 测绘学报, 2015, 44(4): 399-406.
[6]	赵世湖, 尹丹, 窦显辉, 郭莉. 单景卫星遥感影像目标运动信息提取技术[J]. 测绘学报, 2015, 44(3): 316-322.
[7]	丰明博刘学赵冬. 多/高光谱遥感图像的投影和小波融合算法[J]. 测绘学报, 2014, 43(2): 158-163.
[8]	余连生,李智勇,文贡坚,杜春. 遥感图像融合技术在潮间带地形提取中的应用[J]. 测绘学报, 2011, 40(5): 0-0.
[9]	许君一,卢秀山,卿熙宏,姚继锋. 基于热传导模型的像素级遥感图像融合[J]. 测绘学报, 2010, 39(6): 566-571.
[10]	窦闻陈云浩 . 光学遥感影像像素级融合的理论框架[J]. 测绘学报, 2009, 38(2): 0-174.