【作者】 姚为；

【导师】 韩敏；

【作者基本信息】 大连理工大学 ， 控制理论与控制工程， 2011， 博士

【摘要】 传感器技术的发展丰富了人类获取信息的手段,而遥感在今天已成为人类获取地面信息的最重要的方式之一。卫星遥感系统为对地观测和地球科学相关领域研究提供的遥感图像数据,类型多样同时包含了丰富的信息。如何利用图像融合技术,对不同来源不同类型的遥感图像数据进行综合利用,准确而高效地提取图像中包含的有用信息,已成为遥感技术应用中的一个关键性问题。针对这一问题,本文展开了对遥感图像融合方法和相关理论的研究。本文的研究工作主要包含以下三个方面的内容：1、提出一种用于实现多光谱遥感图像分辨率增强的全色锐化图像融合方法。像素级的图像融合方法以提升图像数据质量为目标,而空间分辨率则是遥感图像质量的一个重要指标。传感器捕获辐射能量有限以及观测受到噪声信号干扰的客观条件限制,使得遥感图像的空间分辨率和光谱分辨率成为一对天然的矛盾。利用全色锐化图像融合技术,对具有高空间分辨率的全色图像和具有高光谱分辨率的多光谱图像进行融合,则可以得到同时具有高空间分辨率和高光谱分辨率的合成图像。为得到高质量的全色锐化融合结果,本文对多光谱图像数据和全色图像数据进行线性回归,并基于标准正交变换设计一种颜色空间变换,在此基础上将成分替换与多分辨率分析的思想相结合,完成对融合方法的构造。研究中通过对比实验,验证了该融合方法性能上的优越性。2、提出一种用于实现热红外遥感图像分辨率增强的热红外锐化图像融合方法。热红外图像提供的地表温度信息,在遥感量化分析的应用中十分关键。热红外锐化主要通过热红外图像和可见光近红外图像间的像素级融合实现,由于热红外图像与可见光近红外图像具有不同的成像性质,使得一般的像素级图像融合方法不能适用于这两类图像间的融合。另一方面,如何在融合过程中充分利用多波段的可见光近红外图像所包含的空间细节信息,也是热红外锐化方法设计中的关键问题。本文利用快速高效的极限学习机神经网络算法建立回归模型,并以回归模型为核心构造了热红外锐化的融合方法。研究中利用实际遥感数据进行实验,验证了所提热红外锐化融合方法的有效性。3、提出一种特征级的遥感图像融合方法,实现地表蒸散发特征信息的量化分析。像素级的图像融合是提升图像数据质量的过程,而特征级的图像融合则是由图像数据集提取信息的过程。从遥感图像提取出反映地面状态的特征参数的过程称为遥感量化分析,蒸散发量等地表特征信息的量化分析是遥感应用技术研究的一类重要问题。蒸散发特征信息的量化过程涉及到众多中间特征参数,需要通过多步复合的特征融合来实现。同时以地表各特征参数间的物理关系和地表结构模型为基础,来构造融合过程中的融合规则。研究中将特征融合得到的结果与地表实测数据对比以验证本文所提特征融合方法的有效性,并利用所提方法来处理湿地遥感图像序列,从而对湿地生态系统状态变化情况进行全面的分析。更多还原

【Abstract】 The development of sensor technology enriched human’s accesses to information, and remote sensing technology nowadays has already become the most important means for human to get information about the earth. Satellite remote sensing systems provide information rich and diverse types of remote sensing image data for the application of earth observation and the study of geosciences. Using image fusion technologies to merge different types of remote sensing images and to accurately and efficiently extract useful information from these images has become a key issue in applications of remote sensing technology. Therefore, we launched a study on the methods and related theories of remote sensing image fusion.The research described in this thesis mainly contains the following three aspects:1, A novel pansharpening fusion method has been proposed aiming at resolution enhancement of multi-spectral remote sensing images. Improvement of image quality is the main concern of pixel-level image fusion, and spatial resolution is the most important quality of remote sensing images. Because the radiation energy captured by the sensors is limited and the observations are usually interferenced by noises, the qualities of high spatial resolution and high spectral resolution can hardly be achieved at the same time in remote sensing images. However, using pansharpening technologies to fuse multi-spectral images with panchromatic image, synthetic images with both high spatial and spectral resolution can be obtained. In order to obtain a pansharpening method with outstanding fusion performance, an elaborately designed color space transform is employed. This color space transform is a standard orthogonal transform based on the linear regression of image data. Furthermore, the idea of multi-resolution analysis is also applied to complete the construction of the fusion method. The superiority of the proposed method has been verified in comparative experiments.2, A thermal sharpening method has been proposed to achieve resolution enhancement of thermal infrared remote sensing images. Thermal infrared images provide information on surface temperature, which is critical in quantitative remote sensing applications, therefore the research of thermal sharpening methods is practically meaningful. Thermal sharpening is achieved on the fusion of thermal infrared image and visual near-infrared images, and due to the different characteristics of these two kinds of remote sensing images, common fusion methods of pixel level can not be used to implement the fusion. On the other hand, how to make full use of the spacial details contained in the multi-channel visual near-infrared images is another essential issue for thermal sharpening. In this thesis, a high-speed neural network algorithm is adopted to establish a regression model as the core structure of the fusion method for thermal sharpening. The efficiency of the proposed thermal sharpening fusion method has been shown in experiments using actual remote sensing data. 3, A feature level remote sensing image fusion method has been proposed to conduct quantitative analysis of surface evapotranspiration information. Pixel level image fusion is the process of upgrading the quality of the image data, while feature level image fusion is the process of extracting information by the integration of multiple remote sensing images. Quantitative analysis of surface information, including evapotranspiration related information, is an important issue in the research of remote sensing technology. A number of intermediate parameters are involved in the process of quantifying surface evapotranspiration, therefore, the subject can be solved through a complex multi-step fusion procedure. The fusion rules are established based on the ground surface structure model and the physical relationship between surface parameters. Feature fusion results are compared with the surface measured data to prove the validity of the proposed method, and a comprehensive understanding of the state of the studying area can be obtained according to these feature fusion results.更多还原