青藏高原中分辩率亚像元雪填图算法研究
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摘要
青藏高原是是全球气候上最为敏感的地区之一,也是我国降雪量最多的地区。利用遥感方法监测青藏高原地表冰雪面积变化,对理解青藏高原气候气象研究有重要意义。积雪是变化最剧烈的地表覆盖类型,在大面积的积雪监测中需要使用高时间分辩率、中低空间分辩率的遥感数据,而中低分辨率雪填图受混合像元困扰。
本文以混合像元分解研究为纲,以实现青藏高原地区自动化高精度的亚像元雪填图为目的,研究了混合像元分解的理论模型、线性混合分解中端元选取问题,并比较了线性混合分解、模糊C均值聚类、BP神经网络及支持向量机在中分辨率雪填图中性能,并开发了自动雪填图算法。
论文共包括六章。第一章介绍了现有雪填图的研究现状及最新进展,并着重分析了Modis冰/雪产品使用的Modis二值分类算法,最后分析了亚像元雪填图面临的问题。第二章介绍了混合像元概念、基于各种不同理论的混合像元模型,并对其进行了分析。第三章简要介绍了在线性混合模型中至关重要的端元选取问题,并对各类端元选取算法进行分析。第四章集中研究了线性混合分解模型、模糊C均值聚类、BP神经网络及支持向量机算法,对四种算法在雪的亚像元填图中的性能进行了深入研究。第五章介绍了自动亚像元雪填图算法的开发,对算法中面临的问题及其解决方案进行了深入探讨。第六章为结论部分。
本论文的研究方法及重点可以归纳为:
1.在线性混合分解中,完全纯净的像元很难找到,算法中所使用的端元总是存在误差,与传统的线性混合分解使用最小二乘算法不同,本文使用考虑端元误差的约束全最小二乘算法进行混合像元分解。
2.在应用模糊C均值聚类进行混合像元分解时,考虑到算法中限定聚类数目会带来‘异常点’问题,引入了新的目标函数;在神经网络进行混合像元分解中,在模拟数据实验的基础上,选用了基于非线性最小二乘法的Levenberg-Marquardt的训练算法;支持向量机算法选用线性不可分模型。
3.开发运行稳定自动化的雪填图算法。地物因地形、太阳入射角、传感器
Snow is an important component of the Earth's surface. Because of its importance from both a scientific and resource management standpoint, accurate monitoring of snow cover extent is an important research goal in the science of Earth systems. The Tibetan Plateau is the most sensitive area in the world to climatic change. Moderate resolution image is currently the most suitable data for monitoring regional snow extent. The high spatial resolution and numerous MODIS spectral bands in the 0.4 to 2.5 μm wavelength region allow more accurate monitoring of snow cover extent on a global basis than is possible with other operational satellites.The goal of this dissertation is to investigate novel methods of remote sensing technologies to improve the accuracy of mapping snow cover. Medium spatial resolution remotely sensed imagery is comparatively very cheap, but has a critical drawback "mixed" pixels. The problem is severe over rugged terrain, where extreme variations in snow cover, vegetation type, canopy density, and lithology occur over small horizontal distances, leading to mis-classification of snow covered areas.Mixture modelling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve the sub-pixel, mixture information, which arises from the overlapping land cover types within the pixel's instantaneous field of view. This paper described and summarized all kinds of mixture models. Selecting spectral endmembers is the key in a spectral unmixng algorithm. In current studies about snow spectral unmixng algorithm, the major difference is the techniques in selecting spectral endmembers. The analyses of the current techniques in selection of the reference spectral endmembers is also done in this papar.In the paper, I tested four different sub-pixel analysis methods: Linear Mixture Model (LMM), Fuzzy c-means Clustering (FCM), Back-Progagation Neural
本文以混合像元分解研究为纲,以实现青藏高原地区自动化高精度的亚像元雪填图为目的,研究了混合像元分解的理论模型、线性混合分解中端元选取问题,并比较了线性混合分解、模糊C均值聚类、BP神经网络及支持向量机在中分辨率雪填图中性能,并开发了自动雪填图算法。
论文共包括六章。第一章介绍了现有雪填图的研究现状及最新进展,并着重分析了Modis冰/雪产品使用的Modis二值分类算法,最后分析了亚像元雪填图面临的问题。第二章介绍了混合像元概念、基于各种不同理论的混合像元模型,并对其进行了分析。第三章简要介绍了在线性混合模型中至关重要的端元选取问题,并对各类端元选取算法进行分析。第四章集中研究了线性混合分解模型、模糊C均值聚类、BP神经网络及支持向量机算法,对四种算法在雪的亚像元填图中的性能进行了深入研究。第五章介绍了自动亚像元雪填图算法的开发,对算法中面临的问题及其解决方案进行了深入探讨。第六章为结论部分。
本论文的研究方法及重点可以归纳为:
1.在线性混合分解中,完全纯净的像元很难找到,算法中所使用的端元总是存在误差,与传统的线性混合分解使用最小二乘算法不同,本文使用考虑端元误差的约束全最小二乘算法进行混合像元分解。
2.在应用模糊C均值聚类进行混合像元分解时,考虑到算法中限定聚类数目会带来‘异常点’问题,引入了新的目标函数;在神经网络进行混合像元分解中,在模拟数据实验的基础上,选用了基于非线性最小二乘法的Levenberg-Marquardt的训练算法;支持向量机算法选用线性不可分模型。
3.开发运行稳定自动化的雪填图算法。地物因地形、太阳入射角、传感器
Snow is an important component of the Earth's surface. Because of its importance from both a scientific and resource management standpoint, accurate monitoring of snow cover extent is an important research goal in the science of Earth systems. The Tibetan Plateau is the most sensitive area in the world to climatic change. Moderate resolution image is currently the most suitable data for monitoring regional snow extent. The high spatial resolution and numerous MODIS spectral bands in the 0.4 to 2.5 μm wavelength region allow more accurate monitoring of snow cover extent on a global basis than is possible with other operational satellites.The goal of this dissertation is to investigate novel methods of remote sensing technologies to improve the accuracy of mapping snow cover. Medium spatial resolution remotely sensed imagery is comparatively very cheap, but has a critical drawback "mixed" pixels. The problem is severe over rugged terrain, where extreme variations in snow cover, vegetation type, canopy density, and lithology occur over small horizontal distances, leading to mis-classification of snow covered areas.Mixture modelling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve the sub-pixel, mixture information, which arises from the overlapping land cover types within the pixel's instantaneous field of view. This paper described and summarized all kinds of mixture models. Selecting spectral endmembers is the key in a spectral unmixng algorithm. In current studies about snow spectral unmixng algorithm, the major difference is the techniques in selecting spectral endmembers. The analyses of the current techniques in selection of the reference spectral endmembers is also done in this papar.In the paper, I tested four different sub-pixel analysis methods: Linear Mixture Model (LMM), Fuzzy c-means Clustering (FCM), Back-Progagation Neural
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