基于小波域隐马尔可夫树模型的遥感图像纹理分类研究
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摘要
遥感图像有着丰富的纹理信息,准确地提取纹理特征对于图像分割或分类至关重要。基于模型的方法是纹理特征提取的基本方法,也是一种比较适用于遥感图像纹理分析的方法。此外,纹理的多尺度效应描述在纹理分析中也不可忽视。
本文根据遥感图像纹理所呈现的多尺度特性和随机的特性,采用基于小波域隐马尔可夫树模型(HMT)对纹理进行分析,并有机地结合遥感图像的要求和特点进行深入研究。由于小波系数不满足高斯分布,在同尺度内和尺度间都表现为一种潜在的依赖关系,所以小波域HMT模型较准确地揭示了小波系数间的这些依赖关系,在实现上利用最大期望算法(EM算法)估计模型参数,采用最大似然方法进行分类。
结合遥感图像的特点,论文在描述HMT模型建立及其图像分割应用的基础上,对模型本身及其在图像中的具体应用进行了深入研究。研究并解决了训练样本与待分图大小不一致的问题;提出一次建模、多级同时分割的展示方法:针对多光谱遥感图像,通过HSI变换和最大主成分分析,将多光谱信息融合起来,得到图像在多方面的描述,如H-S-I分量和最大主成分分量,进而在融合结果上采用HMT方法取得分割结果。研究表明,合理地利用多波段信息有助于分割结果的改进。此外,论文还对基于HMT表示的纹理的多尺度效应及相关问题进行了比较深入的研究,对于后续研究工作具有一定的价值和意义。
Remote sensing image has rich texture information, and exact texture feature extraction is a key factor to both image segmentation and classification. Model-based method is a basic method to the texture feature extraction. It is also a method appropriate to remote sensing image texture analysis.Considering the multi-scale characteristic and the random feature of the remote sensing image, the wavelet-based HMT model is adapted to analyze the texture characteristic. Further research is carried out according to the requirement and characteristic of remote sensing image. Because the wavelet coefficients don't accord with the Gaussian distributions and there are underlying relationships among these wavelet coefficients on both the same scale and the inter-scales, the wavelet-domain HMT model reveals exactly the dependencies among these coefficients. It estimates the model parameters using the expectation maximization (EM) algorithm and processes image classification using Marxism Likelihood (ML) method.Concerning with the characteristics of remote sensing image, based on the description of the basic model establishment and segmentation, further study is conducted in this thesis on the wavelet-domain HMT model itself and its application in remote sensing images. It studies and resolves the problem of the non- consistent size of the training sample with the original image. A method is presented to segment image with once model establishing, multi-layer segmentation results getting at the same time. Aiming at the multi-spectral remote sensing image, the spectral information is fused through HIS transform or PCA (Principle Component Analysis) ,from which we can get the description of different aspects in image. The HMT is then applied to the fused image to get the new segmentation result. The experimental results show that, it helps improving the segmentation result when appropriately using the multi-spectral information. Besides, this thesis deeply studies the multi-scale effect of HMT-based texture description and some related problems. The results may be of use to the future research work.
本文根据遥感图像纹理所呈现的多尺度特性和随机的特性,采用基于小波域隐马尔可夫树模型(HMT)对纹理进行分析,并有机地结合遥感图像的要求和特点进行深入研究。由于小波系数不满足高斯分布,在同尺度内和尺度间都表现为一种潜在的依赖关系,所以小波域HMT模型较准确地揭示了小波系数间的这些依赖关系,在实现上利用最大期望算法(EM算法)估计模型参数,采用最大似然方法进行分类。
结合遥感图像的特点,论文在描述HMT模型建立及其图像分割应用的基础上,对模型本身及其在图像中的具体应用进行了深入研究。研究并解决了训练样本与待分图大小不一致的问题;提出一次建模、多级同时分割的展示方法:针对多光谱遥感图像,通过HSI变换和最大主成分分析,将多光谱信息融合起来,得到图像在多方面的描述,如H-S-I分量和最大主成分分量,进而在融合结果上采用HMT方法取得分割结果。研究表明,合理地利用多波段信息有助于分割结果的改进。此外,论文还对基于HMT表示的纹理的多尺度效应及相关问题进行了比较深入的研究,对于后续研究工作具有一定的价值和意义。
Remote sensing image has rich texture information, and exact texture feature extraction is a key factor to both image segmentation and classification. Model-based method is a basic method to the texture feature extraction. It is also a method appropriate to remote sensing image texture analysis.Considering the multi-scale characteristic and the random feature of the remote sensing image, the wavelet-based HMT model is adapted to analyze the texture characteristic. Further research is carried out according to the requirement and characteristic of remote sensing image. Because the wavelet coefficients don't accord with the Gaussian distributions and there are underlying relationships among these wavelet coefficients on both the same scale and the inter-scales, the wavelet-domain HMT model reveals exactly the dependencies among these coefficients. It estimates the model parameters using the expectation maximization (EM) algorithm and processes image classification using Marxism Likelihood (ML) method.Concerning with the characteristics of remote sensing image, based on the description of the basic model establishment and segmentation, further study is conducted in this thesis on the wavelet-domain HMT model itself and its application in remote sensing images. It studies and resolves the problem of the non- consistent size of the training sample with the original image. A method is presented to segment image with once model establishing, multi-layer segmentation results getting at the same time. Aiming at the multi-spectral remote sensing image, the spectral information is fused through HIS transform or PCA (Principle Component Analysis) ,from which we can get the description of different aspects in image. The HMT is then applied to the fused image to get the new segmentation result. The experimental results show that, it helps improving the segmentation result when appropriately using the multi-spectral information. Besides, this thesis deeply studies the multi-scale effect of HMT-based texture description and some related problems. The results may be of use to the future research work.
引文
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