非下采样Shearlet变换与参数化对数图像处理相结合的遥感图像增强

陶飞翔; 吴一全

doi:10.11947/j.AGCS.2015.20140466

测绘学报 >

2015 , Vol. 44 >Issue 8: 884 - 892

DOI: https://doi.org/10.11947/j.AGCS.2015.20140466

摄影测量学与遥感

非下采样Shearlet变换与参数化对数图像处理相结合的遥感图像增强

陶飞翔 ,
吴一全

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1. 南京航空航天大学电子信息工程学院, 江苏南京 210016;
2. 国土资源部地质信息技术重点实验室, 北京 100037;
3. 兰州大学甘肃省西部矿产资源重点实验室, 甘肃兰州 730000;
4. 江西省数字国土重点实验室, 江西南昌 330013;
5. 中国地质科学院矿产资源研究所国土资源部成矿作用与资源评价重点实验室, 北京 100037

陶飞翔(1990-),男,硕士生,主要从事遥感图像处理等方向的研究。E-mail:nuaatfx@163.com

收稿日期: 2014-09-10

修回日期: 2015-04-07

网络出版日期: 2015-09-02

基金资助

国家自然科学基金(60872065);国土资源部地质信息技术重点实验室开放基金(217);兰州大学甘肃省西部矿产资源重点实验室开放基金(WCRMGS-2014-05);国土资源部成矿作用与资源评价重点实验室开放基金(ZS1406);江西省数字国土重点实验室开放基金(DLLJ201412);江苏高校优势学科建设工程项目

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Remote Sensing Image Enhancement Based on Non-subsampled Shearlet Transform and Parameterized Logarithmic Image Processing Model

TAO Feixiang ,
WU Yiquan

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1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. Key Laboratory of Geological Information Technology, Ministry of Land and Resources, Beijing 100037, China;
3. Key Laboratory of Western China's Mineral Resources of Gansu Province, Lanzhou University, Lanzhou 730000, China;
4. Jiangxi Province Key Laboratory for Digital Land, East China Institute of Technology, Nanchang 330013, China;
5. MLR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China

Received date: 2014-09-10

Revised date: 2015-04-07

Online published: 2015-09-02

Supported by

The National Natural Science Foundation of China(No. 60872065);Open Research Fund of MLR Key Laboratory of Geological Information Technology(No. 217);Open Research Fund of Key Laboratory of Western China's Mineral Resources of Gansu Province(No. WCRMGS-2014-05);Open Research Fund of MLR Key Laboratory of Metallogeny and Mineral Assessment(No.ZS1406);Open Research Fund of Jiangxi Province Key Laboratory for Digital Land(No.DLLJ201412);The Priority Academic Program Development of Jiangsu Higher Education Institution

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摘要

针对部分遥感图像整体亮度偏暗、对比度较低的特点,为提高遥感图像的视觉效果和可解译性,提出了一种基于非下采样Shearlet变换(non-subsampled shearlet transform, NSST)和参数化对数图像处理(parameterized logarithmic image processing, PLIP)模型的遥感图像增强方法。首先,遥感图像经非下采样Shearlet变换分解成低频分量和高频分量;然后依据PLIP模型对其低频分量进行增强,提高图像的对比度;同时利用改进的模糊增强方法对高频分量进行增强,突显边缘细节信息。大量试验结果表明,与双向直方图均衡增强、基于平稳小波变换的增强、基于非下采样Contourlet变换的增强等5种图像增强方法相比,本文提出的方法在主观视觉效果和对比度、清晰度等客观定量评价指标两个方面均有优势,能更有效地提高遥感图像的对比度、增强边缘纹理细节信息,视觉效果更佳。

关键词： 遥感图像; 图像增强; 非下采样Shearlet变换(NSST); 参数化对数图像处理(PLIP)模型; 模糊增强

本文引用格式

陶飞翔 , 吴一全 . 非下采样Shearlet变换与参数化对数图像处理相结合的遥感图像增强[J]. 测绘学报, 2015 , 44(8) : 884 -892 . DOI: 10.11947/j.AGCS.2015.20140466

Abstract

Aiming at parts of remote sensing images with dark brightness and low contrast, a remote sensing image enhancement method based on non-subsampled Shearlet transform and parameterized logarithmic image processing model is proposed in this paper to improve the visual effects and interpretability of remote sensing images. Firstly, a remote sensing image is decomposed into a low-frequency component and high frequency components by non-subsampled Shearlet transform.Then the low frequency component is enhanced according to PLIP (parameterized logarithmic image processing) model, which can improve the contrast of image, while the improved fuzzy enhancement method is used to enhance the high frequency components in order to highlight the information of edges and details. A large number of experimental results show that, compared with five kinds of image enhancement methods such as bidirectional histogram equalization method, the method based on stationary wavelet transform and the method based on non-subsampled contourlet transform, the proposed method has advantages in both subjective visual effects and objective quantitative evaluation indexes such as contrast and definition, which can more effectively improve the contrast of remote sensing image and enhance edges and texture details with better visual effects.

Key words： remote sensing image; image enhancement; non-subsampled Shearlet transform(NSST); parameters logarithmic image processing (PLIP) model; fuzzy enhancement

参考文献

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