联合稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类

零样本影像分类技术旨在通过学习数据集的部分类别（可见类），获得识别在训练阶段未出现类别（不可见类）的能力。该技术在遥感大数据时代具有重要现实意义。目前，遥感领域的零样本场景分类方法对于映射后的语义空间优化关注很少，导致已有方法的整体分类性能较差。基于这一考虑，本文提出了一种基于稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类方法。在训练的有监督学习模块，基于可见类的类别语义向量和场景影像样本，实现深度特征提取器学习和视觉空间到语义空间的稳健映射。在训练的无监督学习阶段，基于全体类别的类别语义向量和不可见类遥感影像样本，分别通过协同表示学习和k近邻算法来渐进修正不可见类类别的语义向量，从而缓解可见类语义空间与不可见类语义空间的漂移问题和自编码跨域映射模型映射后不可见类语义空间与协同表示后不可见类语义空间的偏移问题。在测试阶段，基于学习所得的深度特征提取器、自编码跨域映射模型和修正后的不见类语义向量，实现对不可见类遥感影像场景的分类。本文整合多个已有公开的遥感影像场景数据集，组建了一个新的遥感影像场景数据集，在此数据集上进行试验。试验结果表明本文提出的算法在多种不同的可见类与不可见类的划分情况下都明显优于已有公开零样本分类方法。

Zero-shot remote sensing image scene classification based on robust cross-domain mapping and gradual refinement of semantic space

Zero-shot classification technology aims to acquire the ability to identify categories that do not appear in the training stage (unseen classes) by learning some categories of the data set (seen classes), which has important practical significance in the era of remote sensing big data. Until now, the zero-shot classification methods in remote sensing field pay little attention to the semantic space optimization after mapping, which results in poor classification performance. Based on this consideration, this paper proposed a zero shot remote sensing image scene classification method based on cross-domain mapping with auto-encoder and collaborative representation learning. In the supervised learning module, based on the class semantic vector of seen class and the scene image sample, the depth feature extractor learning and robust mapping from visual space to semantic space are realized. In the unsupervised learning stage, based on the class semantic vectors of all classes and the unseen remote sensing image samples, collaborative representation learning and k-nearest neighbor algorithm are used to modify the semantic vectors of unseen classes, so as to alleviate the problem of the shift of seen class semantic space and unseen class semantic space one after another and unseen after self coding cross domain mapping model mapping the shift of class semantic space and unseen class semantic space after collaborative representation. In the testing phase, based on the depth feature extractor, self coding cross domain mapping model and modified unseen class semantic vector, the classification of unseen class remote sensing image scene can be realized. We integrate a number of open remote sensing image scene data sets and build a new remote sensing image scene data set, experiments were conducted using this dataset The experimental results show that the algorithm proposed in this paper were significantly better than the existing zero shot classification method in the case of a variety of seen and unseen classes.