高分辨率遥感影像已广泛用于环境保护和资源勘探等领域，但影像目标内容描述还不完备，亟需对地类本质特征的分析。本项目把特征对特定地类的依赖性或重要性作为其诊断能力，研究面向特定类别的特征选择方法，在三种情况下开展特征诊断能力分析与选择研究：1)处理大数据量时，不考虑特征间冗余性和噪声样本，建立特征对特定地类的依赖性准则并定量计算依赖性；2)考虑特征间冗余性时，分析特征间的语义相似度，提出相似度传播方法并划分特征视角，然后根据特征重要性选择代表性视角和视角中代表性特征；3)考虑噪声样本时，在选择前后的数据性质差异最小化的条件下，根据样本和特征的重要性同时选择样本与特征。这些方法能突破相关方法很少且信息利用不足等缺陷，探索特征与地类之间的映射关系，初步建立典型区域常见地类的特征库，为识别或智能检索等应用提供诊断特征，并为高分辨率遥感影像的认知模式和机器理解提供理论与实验支撑。

The very high-resolution (VHR) remote sensing images have been widely used in areas such as environmental protection and resource exploration. Unfortunately, the descriptions of the targets in the images are still incomplete, and require analysis on the nature of features for land classes. To address this issue, we plan to take the dependencies or importance of features on particular classes as the diagnostic capabilities of the features, evaluate the capabilities and select diagnostic features by developing class-specific feature selection methods for three situations: 1) when dealing with a large amount of data, we will propose dependency evaluateion criteria of features for specific land classes, make quantitative assessment without considering the noisy samples and the redundancy between features; 2) when considering the redundancy between features, we will compute the semantic similarities between features, partition views of features based on the similarity propagation, and select representative views and their representative features based on the importance of the features; 3) when considering the noisy samples, we will simultaneously select features and samples according to their importance under the condition of minimizing the differences of data nature between before and after selection. The above-mentioned methods will exceed the few related methods in some respects, such as making full use of information. By the methods, the project will explore the mapping between features and classes, initially establish a feature repository for common land classes of typical regions, offer diagnostic features for identification or intelligent retrieval, and provide theoretical and experimental bases for cognitive pattern and machine learning of VHR remote sensing images.