In recent years, active learning (AL) methods have provided a feasible approach to alleviate the problem of limited labeled samples in deep learning projects. Existing AL algorithms generally tend to select sample without labeled, whose category is difficult to distinguish. However, the sample in the category center is difficult to determine in AL operations, resulting in inaccurate category measuring and inaccurate sample selection. In addition, hyperspectral images (HSIs) have rich spectral reflective bands with strong correlations, which leads to the phenomenon that the spatial distribution between different categories in HSIs characterizes staggered distribution, which undoubtedly influences the HSI classification effect. In this article, we propose a new AL method (called PLGCN) which combines prototype learning (PL) and graph convolution network (GCN) to solve few-shot HSI classification tasks, and this method can add into existing deep learning-based HSI classification models. It includes two advantages: 1) the prototype of each category is iteratively updated to ensure the optimality of prototype in each sampling stage and 2) the spatial distribution of unlabeled samples is extracted via graph convolution neural network in order to obtain the better features in new space for easier discriminating. Experimental results on three commonly used benchmark HSI datasets demonstrate the effectiveness of the PLGCN in HSI classification tasks with limited labeled samples.

Deep neural networks (DNNs) have promoted much of the recent progress in hyperspectral image (HSI) classification, which depends on extensive labeled samples and deep network structure and has achieved surprisingly good generalization capacity. However, due to the expensive labeling cost, the labeled samples are scarce in most practice cases, which causes these DNN-based methods to be prone to over-fitting and influences the classification result. To mitigate this problem, we present a clustering-inspired active learning method for enhancing the HSI classification result, which mainly contributes to two aspects. On one hand, the modified clustering by fast search and find of peaks clustering method is utilized to select highly informative and diverse samples from unlabeled samples in the candidate set for manual labeling, which empowers us to appropriately augment the limited training set (i.e., labeled samples) and thus improves the generalization capacity of the baseline DNN model. On the other hand, another K-means clustering-based pseudo-labeling scheme is utilized to pre-train the DNN model with all samples in the candidate set. By doing this, the pre-trained model can be effectively generalized to unlabeled samples in the testing set after being fine tuned-based on the augmented training set. The experiment accuracies on two benchmark HSI datasets show the effectiveness of the proposed method.

Deep neural networks have underpinned much of the recent progress in the field of hyperspectral image (HSI) classification owing to their powerful ability to learn discriminative features. However, training a deep neural network often requires the availability of a large number of labeled samples to mitigate over-fitting, and these labeled samples are not always available in practical applications. To adapt the deep neural network-based HSI classification approach to cases in which only a very limited number of labeled samples (i.e., few or even only one labeled sample) are provided, we propose a novel few-shot deep learning framework for HSI classification. In order to mitigate over-fitting, the framework borrows supervision from an auxiliary set of unlabeled samples with soft pseudo-labels to assist the training of the feature extractor on few labeled samples. By considering each labeled sample as a reference agent, the soft pseudo-label is assigned by computing the distances between the unlabeled sample and all agents. To demonstrate the effectiveness of the proposed method, we evaluate it on three benchmark HSI classification datasets. The results indicate that our method achieves better performance relative to existing competitors in few-shot and one-shot settings.

Few-shot learning (FSL) is an effective approach to address the issue of limited labeled data in hyperspectral image classification (HSIC). However, it overlooks the domain shift between the source and target domains in cross-domain tasks. Most existing domain adaptation methods alleviate the domain shift problem to some extent, but domain adaptation methods based on traditional convolutional operators overlook the non-local spatial relationships in HSI, while methods based on graph neural networks (GNNs), although effective in leveraging non-local spatial information for domain alignment, overly emphasize global relationships, which is disadvantageous for pixel-level classification in HSI. To solve these issues, this paper propose a novel cross-domain few-shot learning framework based on global-local graph attention networks (GLGAT-CFSL), which comprehensively reduces domain shift through global-to-local domain alignment. It has the following advantages: (1) an innovative dynamic triplet graph attention network is devised to identify non-local spatial relationships in HSI for global graph alignment, while also addressing common over-fitting and over-smoothing issues in GNNs; (2) an ingenious local similarity learning strategy is designed after global domain alignment, utilizing intra-domain connectivity structures and inter-domain node similarities for local domain adaptation, promoting cross-domain information propagation and more comprehensive reduction of domain shift; (3) we propose a novel triaxial dynamic convolutional neural network as the feature extractor, promoting cross-dimensional interaction between spectral and spatial dimensions, establishing a more generalizable and rich feature representation between the source and target domains. Experimental results on three HSI datasets demonstrate the superiority and effectiveness of the proposed GLGAT-CFSL.

In a hyperspectral image classification (HSIC) task, manually labeling samples requires a lot of manpower and material resources. Therefore, it is of great significance to use small samples to achieve the HSIC task. Recently, convolutional neural networks (CNNs) have shown remarkable performance in HSIC, but they still have some areas for improvement. (1) Convolutional kernel weights are determined through initialization and cannot be adaptively adjusted based on the input data. Therefore, it is difficult to adaptively learn the structural features of the input data. (2) The convolutional kernel size is single per layer, which leads to the loss of local information for a large convolutional kernel or global information for a small convolutional kernel. In order to solve the above problems, we propose a plug-and-play method called dynamic convolution based on structural re-parameterization (DCSRP). The contributions of this method are as follows. Firstly, compared with traditional convolution, dynamic convolution is a non-linear function, so it has more representation power. In addition, it can adaptively capture the contextual information of input data. Secondly, the large convolutional kernel and the small convolutional kernel are integrated into a new large convolutional kernel. The large convolutional kernel shares the advantages of the two convolution kernels, which can capture global information and local information at the same time. The results in three publicly available HSIC datasets show the effectiveness of the DCSRP.