一种高分辨率遥感图像去雾霾方法
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摘要
基于遥感多光谱图像中不同谱段对雾霾的不同探测能力,利用对雾霾敏感的蓝谱段构建了雾霾图和非雾霾图;利用对雾霾不敏感的红谱段并采用mean-shift分割来识别亮目标,并对雾霾图进行了修正和补偿,完成了去雾霾处理。以国产高分辨率遥感图像为测试对象,基于常用高分辨率遥感图像质量参数对校正效果进行了评价。研究结果表明,各谱段去雾霾处理后遥感图像质量参数较校正前均得到了提升,非雾霾区域校正后的图像光谱色彩特征与校正前保持了良好的一致性,校正后雾霾区域的细节信息得到了恢复。
Based on the different ability for detecting haze of each spectral band of remote sensing multispectral image, the blue spectral band which is more sensitive to haze is used to label the haze and non-haze images. The red spectral band which is less sensitive to the haze and the mean-shift segmentation are used to label bright object. Bright object can correct and compensate the haze images before dehazing processing. Taking domestic high-resolution remote sensing image as test object, the correction effect is evaluated based on the quality parameters of high-resolution remote sensing images. The research result shows that all image quality parameters of each spectral band are improved after correction, and image features in non-haze area keep consistency before and after correction. Detailed information of image in haze area is recovered and image quality is significantly improved after correction.
Based on the different ability for detecting haze of each spectral band of remote sensing multispectral image, the blue spectral band which is more sensitive to haze is used to label the haze and non-haze images. The red spectral band which is less sensitive to the haze and the mean-shift segmentation are used to label bright object. Bright object can correct and compensate the haze images before dehazing processing. Taking domestic high-resolution remote sensing image as test object, the correction effect is evaluated based on the quality parameters of high-resolution remote sensing images. The research result shows that all image quality parameters of each spectral band are improved after correction, and image features in non-haze area keep consistency before and after correction. Detailed information of image in haze area is recovered and image quality is significantly improved after correction.
引文
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[2] Tan R T. Visibility in bad weather from a single image[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1-8.
[3] Vermote E F, Tanre D, Deuze J L, et al. Second simulation of the satellite signal in the solar spectrum, 6S: an overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 675-686.
[4] Zhou L Y. Research on information compensation theory of visible-spectrum cloudy remote sensing imagery[D]. Zhengzhou: Information Engineering University, 2011: 16-24. 周丽雅. 受云雾干扰的可见光遥感影像信息补偿技术研究[D]. 郑州: 信息工程大学, 2011: 16-24.
[5] Guo F, Tang J, Cai Z X. Image dehazing based on haziness analysis[J]. International Journal of Automation and Computing, 2014, 11(1): 78-86.
[6] He K M, Sun J, Tang X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353.
[7] Bi D Y, Ge Y, Li Q H, et al. A research on defogging methods with single image[J]. Journal of Air Force Engineering University (Natural Science Edition), 2013, 14(6): 46-53. 毕笃彦, 葛渊, 李权合, 等. 单幅图像去雾方法研究[J]. 空军工程大学学报(自然科学版), 2013, 14(6): 46-53.
[8] Narasimhan S, Nayar S. Vision and the atmosphere[J]. International Journal of Computer Vision, 2002, 48(3): 233-254.
[9] Makarau A, Richter R, Müller R, et al. Spectrally consistent haze removal in multispectral data[C]. Proceedings of SPIE, 2014, 9244: 924422.
[10] Makarau A, Richter R, Müller R, et al. Haze detection and removal in remotely sensed multispectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5895-5905.
[11] Scaramuzza P L, Bouchard M A, Dwyer J L. Development of the landsat data continuity mission cloud-cover assessment algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1140-1154.
[12] Xu J W, Zhou Y, Xue X G, et al. Optical remote sensing image defogging algorithm based on DCM-HTM[J]. Journal of Geomatics Science and Technology, 2016, 33(5): 513-519. 许继伟, 周杨, 薛现光, 等. 光学遥感影像DCM-HTM去雾算法研究[J]. 测绘科学技术学报, 2016, 33(5): 513-519.
[13] Jiang H B, Luo S K, Cao D J, et al. Technology of high-density and high-resolution camera of GF-2 satellite[J]. Spacecraft Recovery & Remote Sensing, 2015, 36(4): 25-33. 姜海滨, 罗世魁, 曹东晶, 等. “高分二号”卫星轻小型高分辨率相机技术[J]. 航天返回与遥感, 2015, 36(4): 25-33.
[14] Ma X M, Tao Z M, Zhang L L, et al. Ground layer aerosol detection technology during daytime based on side-scattering lidar[J]. Acta Optica Sinica, 2018, 38(4): 0401005. 麻晓敏, 陶宗明, 张璐璐, 等. 侧向散射激光雷达探测白天近地面气溶胶探测技术[J]. 光学学报, 2018, 38(4): 0401005.
[15] Chavez P S Jr. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data[J]. Remote Sensing of Environment, 1988, 24(3): 459-479.
[16] Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.
[17] Mayer A, Greenspan H. An adaptive mean-shift framework for MRI brain segmentation[J]. IEEE Transactions on Medical Imaging, 2009, 28(8): 1238-1250.
[18] Baum B A, Spinhirne J D. Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 3. Cloud overlap[J]. Journal of Geophysical Research: Atmospheres, 2000, 105(D9): 11793-11804.