基于多极化SAR影像的土地利用/土地覆盖变化检测方法研究
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摘要
土地利用/土地覆盖变化检测是多极化SAR影像的一个重要应用。但由于多极化SAR影像具有的复杂的电磁特性和受噪声影响严重等特点,使得多极化SAR影像的变化检测变得困难。论文主要针对多极化SAR影像自身的特点和变化检测中存在的问题,开展多极化SAR影像土地利用/土地覆盖变化检测研究,包括不同极化SAR影像的融合方法研究和变化检测算法研究。其中极化影像的融合是多极化SAR影像变化检测中的关键技术之一,对其进行研究可为后续的变化检测研究奠定基础;变化检测方法的研究从像素级和对象级两个层次分别进行。
论文的主要工作和创新点包括:
1.对极化SAR影像的特性进行了论述与分析,并从几何特性、辐射特性以及分辨率特征三个方面详细比较了SAR影像与光学影像的特性差异,以充分了解极化SAR影像的特性,为下一步变化检测算法的研究奠定基础。
2.相干斑噪声是影响SAR影像解译与应用的一个重要因素。本文对多极化SAR影像的相干斑噪声抑制方法进行了总结与评述,并通过实例验证了几种典型噪声抑制算法的局限性。鉴于目前的去噪方法都存在着不同程度的边缘模糊和细节损失问题,为最大限度地保留影像原始信息,本文的研究采取了不预先对影像进行去噪处理,而是通过变化检测算法本身来降低相干斑噪声的影响的策略,在一定程度上解决了“噪声抑制和细节保持”的难点问题。
3.提出了一套基于NSCT变换域的多极化SAR影像融合方法,包括同一波段不同极化SAR影像的融合方法和不同波段不同极化SAR影像的融合方法。该方法可以较好地综合与增强原影像的细节信息,同时可以提高信噪比,同小波融合法等方法相比,具有较大的优势。
4.提出了一种新的基于多尺度特征级融合的NSCT域多极化SAR图像变化检测算法。该算法通过对NSCT多尺度分解的低频子带系数进行处理来降低斑点噪声的影响;将多尺度、特征级融合策略引入到变化检测算法中,较好地实现了不同尺度、不同极化信息的融合与增强。
5.针对面向对象变化检测中,如何在缺乏GIS辅助数据的情况下,实现不同时相影像分割结果的一一对应,提出了一种新的基于单时相影像分割的多极化SAR影像面向对象变化检测方法。该方法通过将一时相影像的分割结果与另一时相影像的套合与特征值比较,实现了无GIS数据支持下的图斑对象的一一对应;并通过对初次变化检测结果的进一步分割与检测,实现变化区域的精确提取。该论文有图35幅,表14个,参考文献163篇。
Land Use and Land Cover Change detection is one of the important applications of multi-polarization SAR images. However, SAR images have complex electromagnetic properties and are seriously affected by speckle noise, which make the application of SAR images particularly difficult. In this dissertation, some major research has been carried out, and new algorithms are presented considering the characteristics of the multi-polarization SAR images and the problems existing in the current change detection algorithms. To focus on multi-polarization SAR image change detection, the polarimetric image fusion, which is one of the key technologies of change detection, was researched first and then the change detection using multi-polarization SAR was implemented at the pixel level and the object level, respectively.
The major tasks and contribution of this dissertation are:
First, characteristics of polarimetric SAR images are discussed and analyzed. To understand the characteristics of multi-polarization SAR images, the differences between SAR images and optical images were compared fully from three aspects: geometric properties, radiation properties and resolution characteristics.
Second, speckle noise usually affects the interpretation and application of SAR images severely. In this dissertation, typical SAR image speckle noise reduction methods have been analyzed, and the performance of these methods has been reviewed by specific experiments. Since no current de-noising methods can perform well in both noise suppression and detail maintenance, this study does not take the image de-noising preprocessing, but reduces speckle noise by the change detection algorithm itself. This has been proven to be a good solution on noise suppression in this dissertation.
Third, a set of NSCT based polarimetric SAR image fusion approaches are presented, including image fusion using different polarimetric SAR images in the same band, and image fusion using different polarimetric SAR images in two different bands. These methods perform better than the wavelet approach in terms of integrating and enhancing the details of the original image and improving the ratio of signal to noise.
Fourth, a novel Change Detection Algorithm based on Multi-scale Feature Level Image Fusion in the NSCT Domain was proposed. In this approach, the noise impacts were reduced only by NSCT multi-scale decomposition and the usage of the low-frequency sub-band coefficients. Moreover, the multi-scale feature level fusion strategies in this change detection algorithm can not only improve image quality, but also reduce the noise further.
Fifth, a novel object-oriented multi-polarization SAR image change detection approach based on single-phase image segmentation was presented. In this method, an image can be segmented with the same polygons of the other image objects, even in the case of the lack of GIS auxiliary data. Moreover, the image objects, which the change parcels extracted during the first process, were segmented again and the changes were further detected. In this way, the change areas were extracted accurately.
Sixth, different experiments on polarimetric SAR image fusion and change detection were carried out using airborne or spaceborne multi-polarization SAR images. By these experiments, some good experimental results have been obtained and a series of valuable conclusions have been drawn.
论文的主要工作和创新点包括:
1.对极化SAR影像的特性进行了论述与分析,并从几何特性、辐射特性以及分辨率特征三个方面详细比较了SAR影像与光学影像的特性差异,以充分了解极化SAR影像的特性,为下一步变化检测算法的研究奠定基础。
2.相干斑噪声是影响SAR影像解译与应用的一个重要因素。本文对多极化SAR影像的相干斑噪声抑制方法进行了总结与评述,并通过实例验证了几种典型噪声抑制算法的局限性。鉴于目前的去噪方法都存在着不同程度的边缘模糊和细节损失问题,为最大限度地保留影像原始信息,本文的研究采取了不预先对影像进行去噪处理,而是通过变化检测算法本身来降低相干斑噪声的影响的策略,在一定程度上解决了“噪声抑制和细节保持”的难点问题。
3.提出了一套基于NSCT变换域的多极化SAR影像融合方法,包括同一波段不同极化SAR影像的融合方法和不同波段不同极化SAR影像的融合方法。该方法可以较好地综合与增强原影像的细节信息,同时可以提高信噪比,同小波融合法等方法相比,具有较大的优势。
4.提出了一种新的基于多尺度特征级融合的NSCT域多极化SAR图像变化检测算法。该算法通过对NSCT多尺度分解的低频子带系数进行处理来降低斑点噪声的影响;将多尺度、特征级融合策略引入到变化检测算法中,较好地实现了不同尺度、不同极化信息的融合与增强。
5.针对面向对象变化检测中,如何在缺乏GIS辅助数据的情况下,实现不同时相影像分割结果的一一对应,提出了一种新的基于单时相影像分割的多极化SAR影像面向对象变化检测方法。该方法通过将一时相影像的分割结果与另一时相影像的套合与特征值比较,实现了无GIS数据支持下的图斑对象的一一对应;并通过对初次变化检测结果的进一步分割与检测,实现变化区域的精确提取。该论文有图35幅,表14个,参考文献163篇。
Land Use and Land Cover Change detection is one of the important applications of multi-polarization SAR images. However, SAR images have complex electromagnetic properties and are seriously affected by speckle noise, which make the application of SAR images particularly difficult. In this dissertation, some major research has been carried out, and new algorithms are presented considering the characteristics of the multi-polarization SAR images and the problems existing in the current change detection algorithms. To focus on multi-polarization SAR image change detection, the polarimetric image fusion, which is one of the key technologies of change detection, was researched first and then the change detection using multi-polarization SAR was implemented at the pixel level and the object level, respectively.
The major tasks and contribution of this dissertation are:
First, characteristics of polarimetric SAR images are discussed and analyzed. To understand the characteristics of multi-polarization SAR images, the differences between SAR images and optical images were compared fully from three aspects: geometric properties, radiation properties and resolution characteristics.
Second, speckle noise usually affects the interpretation and application of SAR images severely. In this dissertation, typical SAR image speckle noise reduction methods have been analyzed, and the performance of these methods has been reviewed by specific experiments. Since no current de-noising methods can perform well in both noise suppression and detail maintenance, this study does not take the image de-noising preprocessing, but reduces speckle noise by the change detection algorithm itself. This has been proven to be a good solution on noise suppression in this dissertation.
Third, a set of NSCT based polarimetric SAR image fusion approaches are presented, including image fusion using different polarimetric SAR images in the same band, and image fusion using different polarimetric SAR images in two different bands. These methods perform better than the wavelet approach in terms of integrating and enhancing the details of the original image and improving the ratio of signal to noise.
Fourth, a novel Change Detection Algorithm based on Multi-scale Feature Level Image Fusion in the NSCT Domain was proposed. In this approach, the noise impacts were reduced only by NSCT multi-scale decomposition and the usage of the low-frequency sub-band coefficients. Moreover, the multi-scale feature level fusion strategies in this change detection algorithm can not only improve image quality, but also reduce the noise further.
Fifth, a novel object-oriented multi-polarization SAR image change detection approach based on single-phase image segmentation was presented. In this method, an image can be segmented with the same polygons of the other image objects, even in the case of the lack of GIS auxiliary data. Moreover, the image objects, which the change parcels extracted during the first process, were segmented again and the changes were further detected. In this way, the change areas were extracted accurately.
Sixth, different experiments on polarimetric SAR image fusion and change detection were carried out using airborne or spaceborne multi-polarization SAR images. By these experiments, some good experimental results have been obtained and a series of valuable conclusions have been drawn.
引文
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