顾及异方差与空间约束的高光谱混合像元模糊聚类分解方法研究

项目名称： 顾及异方差与空间约束的高光谱混合像元模糊聚类分解方法研究

项目编号： No.41501410

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 徐林林

作者单位： 中国地质大学（北京）

项目金额： 20万元

中文摘要： 高光谱混合像元分解一直是研究热点和难点，关键在于光谱混合模型构建与参数估计，目的是获取高精度的参数估值并应用。目前方法在模型构建上没有考虑噪声的异方差性和丰度的大尺度空间相关性，在参数估计上没有考虑端元的净化均值特征和丰度的模糊标签效应，导致模型参数估计精度损失和优化计算困难。.本项目引入异方差和空间相关性以构造新的线性光谱混合模型，并研究基于模糊聚类优化该新模型的方法，研究包括：（1）噪声异方差模型的构建；（2）大尺度空间相关性模型的构建；（3）用净化均值替代端元复杂先验分布的优化算法；（4）将非负丰度系数作为模糊标签以形成模糊聚类算法；理论上，是对提高高光谱遥感数据建模精度并优化算法的贡献；应用上，该模型不仅可用于非监督混合像元分解、端元提取，并提高精度，而且可以用于高光谱去噪、特征提取、空间聚类、目标探测、图像压缩等其它高光谱图像处理，具有较广的实用价值。

中文关键词： 高光谱混合像元分解；空间相关性；噪声异方差结构；模糊标签；净化均值聚类

英文摘要： Hyperspectral unmixing is a popular yet challenging research topic, with the key issue being efficient model construction and optimization to obtain accurate model parameters. Nevertheless, in terms of model construction, the current methods fail to address the noise heterogeneity effect and the spatial correlation effect in abundance coefficients, and in terms of model optimization, they fail to consider the purified means effect of the endmembers and the fuzzy class membership effect of the abundances coefficients, causing compromised model efficiency and difficulties in model optimization. The project therefore focuses on addressing the noise heterogeneity effect and spatial correlation effect for building enhanced linear spectral model, and focuses on building an efficient optimization scheme using fuzzy clustering approach, including, (1) the construction of heterogeneous noise model; (2) the modeling of spatial correlation effect; (3) the use of endmembers as purified means for avoiding complex prior distribution of endmembers; (4) the use of abundance coefficients as fuzzy class membership for building a fuzzy-clustering-based optimization algorithm. From a theoretical perspective, the proposed model provides new ways for modeling and optimizing the spectral unmixing issue. From an application perspective, the proposed model not only can be used for enhanced spectral unmxing and endmember extraction, but also can be used for other hyperspectral image processing tasks, such as denoising, feature extraction, clustering analysis, target detection and compression.

英文关键词： Hyperspectral Unmixing;Spatial correlation effect;Noise heterogeneity effect;fuzzy class membership ;purified means clustering