高光谱遥感影像联合字典学习与分类研究

Joint dictionary learning is an important research direction in sparse representation, which allows for jointly learning the reconstructive and discriminative dictionary and classifier. This research focuses on hyperspectral imaging mechanisms and hyperspectral image (HSI) characteristics and aims at proposing a classification scheme for hyperspectral image based on joint dictionary learning. To this end, three joint dictionary learning algorithms for HSI classification will be proposed. Firstly, active and semi-supervised learning will be combined with joint dictionary learning, which will take full advantage of the unlabeled samples to learn the dictionary by adopting semi-supervised learning (SSL). In this phase, active learning (AL) methods are adopted to choose the most informative and unbiased unlabeled samples in order for SSL to work well. Therefore, the active semi-supervised dictionary learning method can obtain more powerfull dictionary and classifier. Secondly, the prior knowledge will be introduced for learning structured sparsity-inducing dictionary, which allows for a sufficient excavation of prior knowledge and rules to overcome the sightlessly design of structure. Thirdly, a class dependent joint sub-dictionary learning algorithm will be proposed, which needs less labeled samples in order to obtain adequate representation power. Moreover, the propsed method will greatly speed up the computation and alleviate the redundant phenomenon in the learned dictionary. To sum up, the research will focus on promoting the performance of joint dictionary learning for HSI classification in terms of the avaibility of training samples, the discrimination of the learned models and the computational efficiency. The outcomes of this research will expand the research of HSI classification based on joint dictionary learning, and further promote the application of sparse representation in remote sensing imaging. Last but not least, the research also has significant perspective in promoting the industrial application of HSI data set.

作为稀疏表达的重要内容，联合字典学习旨在建立具有重建能力和判别能力的字典和同步学习分类器，以提升分类性能。本项目针对高光谱遥感信息机理和影像特点，构建基于联合字典学习的高光谱遥感影像分类框架，实现三个有效的分类算法。首先，提出主动半监督联合字典学习方法，利用主动学习为半监督学习选择信息量大、无偏的未标记样本，使字典学习过程更有效，获得更好的字典和分类器。其次，构建先验知识引导、结构化稀疏诱导的联合字典学习方法，充分挖掘先验知识和规则，克服稀疏诱导规则的盲目性。第三，提出类别依赖的联合亚字典学习方法，利用较少的已标记样本获得对类别足够好的表达力，提高计算效率，避免字典冗余现象。项目从样本有限性、模型判别性和算法效率三个角度全面提升联合字典学习用于高光谱影像分类的性能。研究成果将拓展基于联合字典学习的高光谱影像分类方法，促进稀疏表达在遥感影像处理中的应用，推进高光谱数据的行业应用。

面向高光谱遥感图像处理与信息提取的需求，以稀疏表达与字典学习为基础理论，构建了基于稀疏表达的高光谱影像联合字典学习与分类方法体系，针对分类中样本有限性、模型判别性和算法效率三个难点，研究了多特征与多任务学习、多属性稀疏特征描述与表达、核选择与多核学习、两步优化策略、多模型稀疏特征融合表达、字典更新策略、语义稀疏字典构建与表达、流数据与在线字典更新、协同稀疏图嵌入等关键技术，提出了针对有限样本的主动半监督联合字典学习、考虑先验知识的结构化诱导联合字典学习、考虑区域特征并能够提高效率的亚字典联合学习等处理策略，在特征多样性稀疏表达、模型多样性稀疏表达、稀疏字典完备性、稀疏判别性等典型高光谱图像处理任务建立了有效的算法模型和处理流程，结合城市/农用地土地覆盖分类、城市功能区划分等实例进行定性、定量评价，提高了分类精度、效率和可靠性，提升了高光谱遥感影像稀疏表达和字典学习的应用水平。围绕项目研究，在IEEE Transactions on Geoscience and Remote Sensing、IEEE Journal of Selected Topics in Signal Processing、IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing、IEEE Geoscience and Remote Sensing Letters等国际权威期刊发表SCI论文10篇，《遥感学报》发表研究论文1 篇，在国际会议发表论文6篇，培养博士生4人、硕士生3人，获得软件著作权2项。.

内容获取失败，请点击重试