基于NSCT变换的高分三号SAR与光学图像融合
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摘要
高分三号卫星是世界上成像模式最多的合成孔径雷达(SAR)卫星,高分三号SAR图像与多光谱图像融合可以改善图像视觉效果。因此提出一种新的研究思路,即利用非下采样轮廓波变换(NSCT)模拟出既包含多光谱谱段信息又体现SAR图像细节信息的高分辨率图像,则融合可不拘泥于具体算法。同时提出两种基于NSCT的高分辨率图像模拟方法,利用高分三号3 m、5 m分辨率SAR图像和高分一号16 m分辨率图像进行实验,采用不同融合算法验证了该思路的有效性。研究结果表明:传统的SAR和多光谱图像直接融合的方法能够保持SAR的细节信息,但噪声明显,且光谱信息损失大;而所提出的NSCT平均图像和平均NSCT图像可以保留融合结果的光谱信息,且模拟的光谱信息前者比后者更贴近多光谱。
Among the existing synthetic-aperture radar(SAR) satellites, the GF-3 offers the most kinds of imaging modes. The fusion of the GF-3 SAR images with the multi-spectral images can improve the visual quality of the SAR images. We show how to use the nonsubsampled contourlet transform(NSCT) for simulating high-resolution images such that both the details of the SAR image and the spectral information of the multi-spectral image can be retained. This method ensures that the fusion of SAR and multi-spectral images is not limited by a specific algorithm. To verify the effectiveness of the proposed idea, two types of resolutions are used as the experimental data: the GF-3 satellite SAR images with resolutions of 3 m and 5 m, respectively, and the GF-1 satellite multi-spectral images with a resolution of 16 m. We perform comparative experiments with different fusion algorithms. The results show the effectiveness of the proposed approach. The traditional method that directly fuses the SAR and multi-spectral images can keep the details of the SAR image. However, the noise is obvious and some information of the multi-spectral image remains. The NSCT average images and the average NSCT images can retain the spectral information. The spectral information of NSCT average images is closer to the multi-spectral images than the average NSCT images.
Among the existing synthetic-aperture radar(SAR) satellites, the GF-3 offers the most kinds of imaging modes. The fusion of the GF-3 SAR images with the multi-spectral images can improve the visual quality of the SAR images. We show how to use the nonsubsampled contourlet transform(NSCT) for simulating high-resolution images such that both the details of the SAR image and the spectral information of the multi-spectral image can be retained. This method ensures that the fusion of SAR and multi-spectral images is not limited by a specific algorithm. To verify the effectiveness of the proposed idea, two types of resolutions are used as the experimental data: the GF-3 satellite SAR images with resolutions of 3 m and 5 m, respectively, and the GF-1 satellite multi-spectral images with a resolution of 16 m. We perform comparative experiments with different fusion algorithms. The results show the effectiveness of the proposed approach. The traditional method that directly fuses the SAR and multi-spectral images can keep the details of the SAR image. However, the noise is obvious and some information of the multi-spectral image remains. The NSCT average images and the average NSCT images can retain the spectral information. The spectral information of NSCT average images is closer to the multi-spectral images than the average NSCT images.
引文
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[8] Huan R H, Zhang P, Pan Y. SAR target recognition using PCA, ICA and Gabor wavelet decision fusion[J]. Journal of Remote Sensing, 2012, 16(2): 262-274. 宦若虹, 张平, 潘赟. PCA、ICA和Gabor小波决策融合的SAR目标识别[J]. 遥感学报, 2012, 16(2): 262-274.
[9] Alparone L, Baronti S, Garzelli A, et al. Landsat ETM+ and SAR image fusion based on generalized intensity Modulation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(12): 2832-2839.
[10] Pal S K, Majumdar T J, Bhattacharya A K. ERS-2 SAR and IRS-1C LISS III data fusion: a PCA approach to improve remote sensing based geological interpretation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 61(5): 281-297.
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[12] Li H H, Guo L, Liu K. SAR and optical image fusion based on Curvelet transform[J]. Journal of Optoelectronics·Laser, 2009, 20(8): 1110-1113. 李晖晖, 郭雷, 刘坤. 基于Curvelet变换的SAR与可见光图像融合研究[J]. 光电子·激光, 2009, 20(8): 1110-1113.
[13] Wang Z S, Yang F B, Chen L, et al. SAR and visible image enhanced fusion based on texture segmentation and Top-Hat transformation[J]. Acta Optica Sinica, 2014, 34(10): 1010002. 王志社, 杨风暴, 陈磊, 等. 基于纹理分割和Top-Hat变换的合成孔径雷达与可见光图像增强融合[J]. 光学学报, 2014, 34(10): 1010002.
[14] Chibani Y. Integration of panchromatic and SAR features into multispectral SPOT images using the 【math88z】 International Journal of Remote Sensing, 2007, 28(10): 2295-2307.
[15] Chen S H, Zhang R H, Su H B, et al. SAR and multispectral image fusion using generalized IHS transform based on 【math89z】 trous wavelet and EMD decompositions[J]. IEEE Sensors Journal, 2010, 10(3): 737-745.
[16] Wan J H, Zang J X, Liu S W. Fusion and classification of SAR and optical image with consideration of polarization characteristics[J]. Acta Optica Sinica, 2017, 37(6): 0628001. 万剑华, 臧金霞, 刘善伟. 顾及极化特征的SAR与光学影像融合与分类[J]. 光学学报, 2017, 37(6): 0628001.
[17] Bai Z G. The technical characteristics of GF-1 satellite[J]. Aerospace China, 2013, (8): 5-9. 白照广. 高分一号卫星的技术特点[J]. 中国航天, 2013, (8): 5-9.
[18] da Cunha A L, Zhou J P, Do M N. The nonsubsampled contourlet transform: theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.
[2] Idol T, Haack B, Mahabir R. Comparison and integration of spaceborne optical and radar data for mapping in Sudan[J]. International Journal of Remote Sensing, 2015, 36(6): 1551-1569.
[3] Ma S, Deng K Z, Zhuang H F, et al. Otsu change detection of low and moderate resolution synthetic aperture radar image by using multi-texture features[J]. Laser & Optoelectronics Progress, 2017, 54(6): 062804. 马骕, 邓喀中, 庄会富, 等. 中低分辨率合成孔径雷达影像多纹理特征的Otsu变化检测[J]. 激光与光电子学进展, 2017, 54(6): 062804.
[4] Zhang L P, Shen H F. Progress and future of remote sensing data fusion[J]. Journal of Remote Sensing, 2016, 20(5): 1050-1061. 张良培, 沈焕锋. 遥感数据融合的进展与前瞻[J]. 遥感学报, 2016, 20(5): 1050-1061.
[5] Bai L Y, Xu C M, Wang C. A review of fusion methods of multi-spectral image[J]. Optik-International Journal for Light and Electron Optics, 2015, 126(24): 4804-4807.
[6] Jia Y H. Fusion of landsat TM and SAR images based on principal component analysis[J]. Remote Sensing Technology and Application, 1998, 13(1): 46-49. 贾永红. TM和SAR影像主分量变换融合法[J]. 遥感技术与应用, 1998, 13(1): 46-49.
[7] Zhang H L, Jiang J J, Wu H A, et al. The BP neural network classification based on the fusion of SAR and TM images[J]. Acta Geodaetica et Cartographica Sinica, 2006, 35(8): 229-233. 张海龙, 蒋建军, 吴宏安, 等. SAR与TM影像融合及在BP神经网络分类中的应用[J]. 测绘学报, 2006, 35(8): 229-233.
[8] Huan R H, Zhang P, Pan Y. SAR target recognition using PCA, ICA and Gabor wavelet decision fusion[J]. Journal of Remote Sensing, 2012, 16(2): 262-274. 宦若虹, 张平, 潘赟. PCA、ICA和Gabor小波决策融合的SAR目标识别[J]. 遥感学报, 2012, 16(2): 262-274.
[9] Alparone L, Baronti S, Garzelli A, et al. Landsat ETM+ and SAR image fusion based on generalized intensity Modulation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(12): 2832-2839.
[10] Pal S K, Majumdar T J, Bhattacharya A K. ERS-2 SAR and IRS-1C LISS III data fusion: a PCA approach to improve remote sensing based geological interpretation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 61(5): 281-297.
[11] Li H H, Guo L, Li G X. Is ridgelet transform better than wavelet transform in SAR and optical image fusion?[J]. Journal of Northwestern Polytechnical University, 2006, 24(4): 418-422. 李晖晖, 郭雷, 李国新. 基于脊波变换的SAR与可见光图像融合研究[J]. 西北工业大学学报, 2006, 24(4): 418-422.
[12] Li H H, Guo L, Liu K. SAR and optical image fusion based on Curvelet transform[J]. Journal of Optoelectronics·Laser, 2009, 20(8): 1110-1113. 李晖晖, 郭雷, 刘坤. 基于Curvelet变换的SAR与可见光图像融合研究[J]. 光电子·激光, 2009, 20(8): 1110-1113.
[13] Wang Z S, Yang F B, Chen L, et al. SAR and visible image enhanced fusion based on texture segmentation and Top-Hat transformation[J]. Acta Optica Sinica, 2014, 34(10): 1010002. 王志社, 杨风暴, 陈磊, 等. 基于纹理分割和Top-Hat变换的合成孔径雷达与可见光图像增强融合[J]. 光学学报, 2014, 34(10): 1010002.
[14] Chibani Y. Integration of panchromatic and SAR features into multispectral SPOT images using the 【math88z】 International Journal of Remote Sensing, 2007, 28(10): 2295-2307.
[15] Chen S H, Zhang R H, Su H B, et al. SAR and multispectral image fusion using generalized IHS transform based on 【math89z】 trous wavelet and EMD decompositions[J]. IEEE Sensors Journal, 2010, 10(3): 737-745.
[16] Wan J H, Zang J X, Liu S W. Fusion and classification of SAR and optical image with consideration of polarization characteristics[J]. Acta Optica Sinica, 2017, 37(6): 0628001. 万剑华, 臧金霞, 刘善伟. 顾及极化特征的SAR与光学影像融合与分类[J]. 光学学报, 2017, 37(6): 0628001.
[17] Bai Z G. The technical characteristics of GF-1 satellite[J]. Aerospace China, 2013, (8): 5-9. 白照广. 高分一号卫星的技术特点[J]. 中国航天, 2013, (8): 5-9.
[18] da Cunha A L, Zhou J P, Do M N. The nonsubsampled contourlet transform: theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.