基于随机场模型的遥感影像变化检测方法研究
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摘要
基于遥感影像的变化检测是一门从不同时间获取的遥感影像中,定量分析和确定地表变化特征和过程的技术。利用不同时相获取的卫星遥感影像以及其他的辅助数据进行变化检测,是开展土地调查、灾害评估、环境监测、基础地理数据库更新等的关键技术,已经在民用和军事领域得到广泛地应用。近年来,如何智能化、完整、准确的从影像中提取变化信息已经成为遥感应用的重要研究课题。对变化检测及相关理论进行深入研究,不但对生产行业具有重大的实际价值,同时也可以推动影像分析和解译相关理论的更好的发展。
本论文主要围绕基于随机场模型的遥感影像变化检测技术中存在的若干关键问题进行研究,主要包括如何智能化的确定变化阂值,如何利用影像所有波段的变化信息以及如何利用影像中多种特征进行检测等问题。具体研究内容如下：
(1)针对当前变化阈值确定方法中分割效率低,自动化程度差,适用性和检测精度上存在不足的缺陷。本章提出一种综合影像像素分布的自适应变化阈值选择方法,该自适应变化阈值选择方法首先对差异影像进行满足终止判定条件的自适应迭代运算,得到初步阈值范围。然后通过分析该阈值边界两侧影像像素的灰度分布情况,结合影像像素的标准差计算最终阈值范围,得出最优变化阈值,以此提取变化区域。在本论文中,利用相应的实验对上述提出的方法进行了验证。实验证明,本文方法的检测精度优于传统的变化检测方法,同时具有更好的稳定性和智能性。
(2)目前,大多数变化检测方法是基于遥感影像单波段进行的,只利用了影像中一个波段的信息,因此难以获取完整的变化信息。针对该问题,本文引入基于马尔可夫随机场(MRF, Markov Random Field)模型的遥感影像变化检测技术,利用MRF模型融合所有波段的变化信息,获得完整的变化信息。在融合的过程中,针对MRF模型参数求解困难问题,利用Expectation-Maximization(EM)算法结合Method of Log-Cumulants(MoLC)模型对所有的波段信息进行迭代运算和模型参数自适应调整,得到最优的MRF模型,以获取更完整准确的变化信息。论文中,通过相关实验对上述提出的方法进行了验证。实验证明,本文方法的检测结果比利用单一波段的传统方法所得到的检测结果更加完整,有效地提高了变化检测的精度。
(3)传统变化检测方法大多基于单一特征进行计算处理,没有综合利用影像所有的特征信息,因而无法完整地检测出影像的全部变化信息。针对该问题,本文提出了一种基于条件随机场(CRF, Condition Random Field)模型的多波段遥感影像变化检测方法。该方法首先计算原始影像对的差异影像,利用不同的准则提取差异影像上的不同特征,并将这些特征组成特征向量。然后,利用CRF模型对特征向量进行融合,最后经过阂值处理,得到更加完整的变化信息。论文中,通过实验对上述提出的方法进行验证。实验证明,利用CRF模型后,可以有效的综合多种影像特征,得到比单一特征更好的检测结果。
本文的主要创新点包括：
(1)提出一种综合影像像素分布的自适应变化阈值选择方法,对差异影像的变化信息进行智能化提取。
(2)提出一种基于MRF模型的遥感影像变化检测技术,利用MRF模型融合影像所有波段的变化信息,获得最终的变化结果。
(3)提出一种基于CRF模型的遥感影像变化检测技术,利用CRF对影像的特征向量集进行融合处理,综合所有影像特征的变化信息,获得最终完整的变化结果。
Change detection technology based on remote sensing image is to quantitatively analysis and to identify changing features and process in remote sensing images at different times. It makes use of the satellite remote sensing image at different time phases as well as other secondary data to conduct change detection, which is a key technology in land survey, disaster assessment, environmental monitoring, basic geographic dataset updating and etc. and has been widely applied to various public and military applications. In recent years, how to intelligentize the process and to precisely extract changing information has become a crucial research subject. The theoretical research in change detection can promote not only the application of this technology in practice production but also the development of related image processing theories.
This dissertation discusses several key problems associated with the remote sensing image cahnge detection based on random fields:how to intelligently determine threshold, and how to fully used changing information of all bands, as well as how to detect by using diverse characteristics in remote sensing image.The concrete contents are as follows:
(1) To solve the lacks of segmentation efficiency, stability, universality and accuracy. This paper proposed a change detection algorithm for remote sensing images based on image fusion and adaptive threshold selection. Firstly, an improved image fusion technology was employed in the process of difference image and ratio image of original data to construct fusion image, based on which, a coarse range of change threshold was achieved by an adaptive iterative operation. Then, by analyzing the discrete levels of the image pixels distributed on both sides of the threshold range, get the final threshold range, and thus get a much more optimal one to extract the final change region. The experimental results suggest that the detection accuracy of this proposed method outperforms the traditional change detection methods, and has certain stability and intelligence.
(2) The most of traditional change detection methods are adopted to deal with the remote sensing images based on single-band information, the all bands information cannot be absolutely used, so it difficult to detect the complete information. To solve this problem, a multi-band remote sensing image change detection algorithm based on the MRF is proposed in the paper, first, the each band change information can be obtained from traditional ratio change detection method, then the multi-band information is fused by MRF model, at last, the optimal change information can be obtained. Because it is difficult to compute the model parameter of MRF, a EM+MoLC model is used to iterate the all bands information, we can obtain the optimal MRF model and the complete change information. The experimental results suggest that the detection accuracy of this method in the paper, which has certain stability and intelligence outperform the traditional change detection methods.
(3) Most of traditional change detection methods conduct computing process based on pure feature, while not comprehensively using all the feature information of remote sensing image, thus cannot be detected changing information completely. To solve this problem, this paper presents a Multispectral change detection method of remote sensing image based on CRF model. This method firstly computes different images based on original image pairs, on which, different features are extracted according to various rules to form feature vectors; then use CRF model to fuse different image feature on feature vectors; finally, through threshold process to get more complete change information. In the paper, we calibrate the proposed method above with appropriate experiments, which proof that, method performed with CRF can effectively integrate diverse image features, to get a comparatively better result than pure feature.
The major contents of this dissertation are outlined as follows:
(1) This paper proposed a change detection algorithm for remote sensing images based on image fusion and adaptive threshold selection, and the change information of different images be automatically distilled.
(2) A multi-band remote sensing image change detection algorithm based on the MRF is proposed in the paper, the multi-band information is fused by MRF model, and the optimal change information can be obtained.
(3) This paper presents a Multispectral change detection method of remote sensing image based on CRF model, and use CRF model to fuse different image feature on feature vectors; finally, through threshold process to get more complete change information.
本论文主要围绕基于随机场模型的遥感影像变化检测技术中存在的若干关键问题进行研究,主要包括如何智能化的确定变化阂值,如何利用影像所有波段的变化信息以及如何利用影像中多种特征进行检测等问题。具体研究内容如下：
(1)针对当前变化阈值确定方法中分割效率低,自动化程度差,适用性和检测精度上存在不足的缺陷。本章提出一种综合影像像素分布的自适应变化阈值选择方法,该自适应变化阈值选择方法首先对差异影像进行满足终止判定条件的自适应迭代运算,得到初步阈值范围。然后通过分析该阈值边界两侧影像像素的灰度分布情况,结合影像像素的标准差计算最终阈值范围,得出最优变化阈值,以此提取变化区域。在本论文中,利用相应的实验对上述提出的方法进行了验证。实验证明,本文方法的检测精度优于传统的变化检测方法,同时具有更好的稳定性和智能性。
(2)目前,大多数变化检测方法是基于遥感影像单波段进行的,只利用了影像中一个波段的信息,因此难以获取完整的变化信息。针对该问题,本文引入基于马尔可夫随机场(MRF, Markov Random Field)模型的遥感影像变化检测技术,利用MRF模型融合所有波段的变化信息,获得完整的变化信息。在融合的过程中,针对MRF模型参数求解困难问题,利用Expectation-Maximization(EM)算法结合Method of Log-Cumulants(MoLC)模型对所有的波段信息进行迭代运算和模型参数自适应调整,得到最优的MRF模型,以获取更完整准确的变化信息。论文中,通过相关实验对上述提出的方法进行了验证。实验证明,本文方法的检测结果比利用单一波段的传统方法所得到的检测结果更加完整,有效地提高了变化检测的精度。
(3)传统变化检测方法大多基于单一特征进行计算处理,没有综合利用影像所有的特征信息,因而无法完整地检测出影像的全部变化信息。针对该问题,本文提出了一种基于条件随机场(CRF, Condition Random Field)模型的多波段遥感影像变化检测方法。该方法首先计算原始影像对的差异影像,利用不同的准则提取差异影像上的不同特征,并将这些特征组成特征向量。然后,利用CRF模型对特征向量进行融合,最后经过阂值处理,得到更加完整的变化信息。论文中,通过实验对上述提出的方法进行验证。实验证明,利用CRF模型后,可以有效的综合多种影像特征,得到比单一特征更好的检测结果。
本文的主要创新点包括：
(1)提出一种综合影像像素分布的自适应变化阈值选择方法,对差异影像的变化信息进行智能化提取。
(2)提出一种基于MRF模型的遥感影像变化检测技术,利用MRF模型融合影像所有波段的变化信息,获得最终的变化结果。
(3)提出一种基于CRF模型的遥感影像变化检测技术,利用CRF对影像的特征向量集进行融合处理,综合所有影像特征的变化信息,获得最终完整的变化结果。
Change detection technology based on remote sensing image is to quantitatively analysis and to identify changing features and process in remote sensing images at different times. It makes use of the satellite remote sensing image at different time phases as well as other secondary data to conduct change detection, which is a key technology in land survey, disaster assessment, environmental monitoring, basic geographic dataset updating and etc. and has been widely applied to various public and military applications. In recent years, how to intelligentize the process and to precisely extract changing information has become a crucial research subject. The theoretical research in change detection can promote not only the application of this technology in practice production but also the development of related image processing theories.
This dissertation discusses several key problems associated with the remote sensing image cahnge detection based on random fields:how to intelligently determine threshold, and how to fully used changing information of all bands, as well as how to detect by using diverse characteristics in remote sensing image.The concrete contents are as follows:
(1) To solve the lacks of segmentation efficiency, stability, universality and accuracy. This paper proposed a change detection algorithm for remote sensing images based on image fusion and adaptive threshold selection. Firstly, an improved image fusion technology was employed in the process of difference image and ratio image of original data to construct fusion image, based on which, a coarse range of change threshold was achieved by an adaptive iterative operation. Then, by analyzing the discrete levels of the image pixels distributed on both sides of the threshold range, get the final threshold range, and thus get a much more optimal one to extract the final change region. The experimental results suggest that the detection accuracy of this proposed method outperforms the traditional change detection methods, and has certain stability and intelligence.
(2) The most of traditional change detection methods are adopted to deal with the remote sensing images based on single-band information, the all bands information cannot be absolutely used, so it difficult to detect the complete information. To solve this problem, a multi-band remote sensing image change detection algorithm based on the MRF is proposed in the paper, first, the each band change information can be obtained from traditional ratio change detection method, then the multi-band information is fused by MRF model, at last, the optimal change information can be obtained. Because it is difficult to compute the model parameter of MRF, a EM+MoLC model is used to iterate the all bands information, we can obtain the optimal MRF model and the complete change information. The experimental results suggest that the detection accuracy of this method in the paper, which has certain stability and intelligence outperform the traditional change detection methods.
(3) Most of traditional change detection methods conduct computing process based on pure feature, while not comprehensively using all the feature information of remote sensing image, thus cannot be detected changing information completely. To solve this problem, this paper presents a Multispectral change detection method of remote sensing image based on CRF model. This method firstly computes different images based on original image pairs, on which, different features are extracted according to various rules to form feature vectors; then use CRF model to fuse different image feature on feature vectors; finally, through threshold process to get more complete change information. In the paper, we calibrate the proposed method above with appropriate experiments, which proof that, method performed with CRF can effectively integrate diverse image features, to get a comparatively better result than pure feature.
The major contents of this dissertation are outlined as follows:
(1) This paper proposed a change detection algorithm for remote sensing images based on image fusion and adaptive threshold selection, and the change information of different images be automatically distilled.
(2) A multi-band remote sensing image change detection algorithm based on the MRF is proposed in the paper, the multi-band information is fused by MRF model, and the optimal change information can be obtained.
(3) This paper presents a Multispectral change detection method of remote sensing image based on CRF model, and use CRF model to fuse different image feature on feature vectors; finally, through threshold process to get more complete change information.
引文
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