Hyperspectral images with an immense number of spectral bands provide abundant discriminant information for accurate land-cover classification in the remote sensing field. However, these narrow and adjacent bands contain a large amount of redundant information. Analyzing these images always requires a huge storage space with expensive computational costs. Furthermore, their high correlation coefficient would lead to the Hughes phenomenon, hindering the improvement of classification performance. We propose a linear semi-supervised hyperspectral feature extraction method L3ME to learn latent local manifold embeddings. Although labeled samples are beneficial to construct learning models, their number is always limited in real-world tasks. The motivation of this paper is to jointly enhance the contributions of labeled and unlabeled samples for learning local manifold structures of hyperspectral images. Features in labeled samples are extracted by two procedures, the adaptive patch alignment framework and integrated intraclass-interclass relationships, from different perspectives. The former aims to solve the problem of the uneven distribution of classes by introducing spectral angle based adaptive parameters. The latter aims to solve the problem of the uneven distribution of samples by constructing several adjacency graphs. The locality preserving projection is capable of preserving the local neighborhood structure of samples. A penalty for sparse regularization is cleverly integrated into the proposed linear discriminant objective function, which is optimized using a novel updating strategy. The convergence of L3ME is proved in detail and analyzed in this paper. Experiments on three typical hyperspectral datasets illustrate the effectiveness of the proposed method over some state-of-the-art techniques. The implementation of L3ME is available at https://github.com/biowby/L3ME.

中文翻译：

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学习用于高光谱特征提取的潜在局部流形嵌入

具有大量光谱带的高光谱图像为遥感领域的准确土地覆盖分类提供了丰富的判别信息。然而，这些窄而相邻的频带包含大量的冗余信息。分析这些图像总是需要巨大的存储空间和昂贵的计算成本。此外，它们的高相关系数会导致休斯现象，阻碍分类性能的提高。我们提出了一种线性半监督高光谱特征提取方法 L3ME 来学习潜在的局部流形嵌入。尽管标记样本有利于构建学习模型，但它们的数量在现实世界的任务中总是有限的。本文的动机是共同增强标记和未标记样本对学习高光谱图像局部流形结构的贡献。标记样本中的特征通过两个过程提取，即自适应补丁对齐框架和集成的类内-类间关系，从不同的角度。前者旨在通过引入基于谱角的自适应参数来解决类分布不均匀的问题。后者旨在通过构建多个邻接图来解决样本分布不均匀的问题。局部保持投影能够保持样本的局部邻域结构。稀疏正则化的惩罚被巧妙地集成到提出的线性判别目标函数中，使用新颖的更新策略对其进行了优化。本文对 L3ME 的收敛性进行了详细的证明和分析。在三个典型的高光谱数据集上的实验说明了所提出的方法在一些最先进的技术上的有效性。L3ME 的实现可在 https://github.com/biowby/L3ME 获得。