Generative adversarial networks (GANs) have achieved many excellent results in hyperspectral image (HSI) classification in recent years, as GANs can effectively solve the dilemma of limited training samples in HSI classification. However, due to the class imbalance problem of HSI data, GANs always associate minority-class samples with fake label. To address this issue, we first propose a semisupervised generative adversarial network incorporating a transformer, called HyperViTGAN. The proposed HyperViTGAN is designed with an external semisupervised classifier to avoid self-contradiction when the discriminator performs both classification and discrimination tasks. The generator and discriminator with skip connection are utilized to generate HSI patches by adversarial learning. The proposed HyperViTGAN captures semantic context and low-level textures to reduce the loss of critical information. In addition, the generalization ability of the HyperViTGAN is improved through the use of data augmentation. Experimental results on three well-known HSI datasets, Houston 2013, Indian Pines 2010, and Xuzhou, show that the proposed model achieves competitive HSI classification performance in comparison with the current state-of-the-art classification models.

The Adaptive Boosting (AdaBoost) classifier is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost classifier directly to pulmonary nodule detection of labeled and unlabeled lung CT images since there are still some drawbacks to ensemble learning method. Therefore, to solve the labeled and unlabeled data classification problem, the semi-supervised AdaBoost classifier using an improved sparrow search algorithm (AdaBoost-ISSA-S4VM) was established. Firstly, AdaBoost classifier is used to construct a strong semi-supervised classifier using several weak classifiers S4VM (AdaBoost-S4VM). Next, in order to solve the accuracy problem of AdaBoost-S4VM, sparrow search algorithm (SSA) is introduced in the AdaBoost classifier and S4VM. Then, sine cosine algorithm and new labor cooperation structure are adopted to increase the global optimal solution and convergence performance of sparrow search algorithm, respectively. Furthermore, based on the improved sparrow search algorithm and adaptive boosting classifier, the AdaBoost-S4VM classifier is improved. Finally, the effective improved AdaBoost-ISSA-S4VM classification model was developed for actual pulmonary nodule detection based on the publicly available LIDC-IDRI database. The experimental results have proved that the established AdaBoost-ISSA-S4VM classification model has good performance on labeled and unlabeled lung CT images.

Hyperspectral image (HSI) classification attempts to classify each pixel, which is an important means of obtaining land–cover knowledge. Hyperspectral images are cubic data with spectral–spatial knowledge and can generally be considered as sequential data alongside spectral dimension. Unlike convolutional neural networks (CNNs), which mainly focus on local relationship models in images, transformers have been shown to be a powerful structure for qualifying sequence data. However, it lacks the excellent ability of CNNs in establishing local relationships in images and cannot perform good generalization in case of insufficient data. In addition, the gradient disappearance problem hinders the convergence stability of deep learning networks as the layers get deeper. To address these problems, we propose a Cascaded Convolution-based Transformer with Densely Connected Mechanism (CDCformer) for hyperspectral image classification. First, we propose a cascaded convolution feature tokenization to extract spectral–spatial information, which will introduce some inductive bias properties of CNN into the transformer. In addition, we design a simple and effective densely connected transformer to enhance feature propagation and transfer memorable information from shallow to deep layers. It efficiently improves the performance of the transformer and extracts more discriminative spectral–spatial features from the HSI. Extensive experimental evaluation of three public hyperspectral data sets shows that CDCformer achieves competitive classification results.

Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, explosive growth in HSIs’ scale and dimensions causes “Curse of dimensionality” and “Hughes phenomenon”. Dimensionality reduction has become an important means to overcome the “Curse of dimensionality”. In hyperspectral images, labeled samples are more difficult to collect because they require many labor and material resources. Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly-labeled samples. The promotion of the supervised dimensionality reduction method to the semi-supervised method is mostly done by graph, which is a powerful tool for characterizing data relationships and manifold exploration. To take advantage of the spatial information of data, we put forward a novel graph construction method for semi-supervised learning, called SLIC Superpixel-based $l_{2,1}$ -norm Robust Principal Component Analysis (SURPCA_2,1), which integrates superpixel segmentation method Simple Linear Iterative Clustering (SLIC) into Low-rank Decomposition. First, the SLIC algorithm is adopted to obtain the spatial homogeneous regions of HSI. Then, the $l_{2,1}$ -norm RPCA is exploited in each superpixel area, which captures the global information of homogeneous regions and preserves spectral subspace segmentation of HSIs very well. Therefore, we have explored the spatial and spectral information of hyperspectral image simultaneously by combining superpixel segmentation with RPCA. Finally, a semi-supervised dimensionality reduction framework based on SURPCA_2,1 graph is used for feature extraction task. Extensive experiments on multiple HSIs showed that the proposed spectral-spatial SURPCA_2,1 is always comparable to other compared graphs with few labeled samples.

Semisupervised support vector machine (S3VM) algorithm mainly depends on the predicted accuracy of unlabeled samples, if lots of misclassified unlabeled samples are added to the training will make the training model performance degrade. Thus, the cuckoo search algorithm (CS) is used to optimize the S3VM which also enhances the model performance of S3VM. Considering that the cuckoo search algorithm is limited to the local optimum problem, a new cuckoo search algorithm based on chaotic catfish effect optimization is proposed. First, use the chaotic mechanism with high randomness to initialize the nest for range expansion. Second, chaotic catfish nest is introduced into the effective competition coordination mechanism after falling into the local optimum, so that the candidate’s nest can jump out of the local optimal solution and accelerate the convergence ability. In the experiment, results show that the improved cuckoo search algorithm is effective and better than the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm on the benchmark functions. In the end, the improved cuckoo search algorithm is used to optimize semisupervised SVM which is applied into oil layer recognition. Results show that this optimization model is superior to the semisupervised SVM in terms of recognition rate and time.

Existing graph-based, semi-supervised hyperspectral image (HSI) classification models often suffer from prolonged execution time due to high computational complexity. In this work, we first propose a fast anchor graph regularization (FAGR) model for large scale, HSI classification. FAGR employs a simple anchor-based graph construction procedure and a new adjacency matrix among anchors to dramatically reduce the computational complexity while attaining good classification performance. In order to further improve the classification accuracy of hyperspectral images, we propose a novel semi-supervised anchor graph ensemble (SAGE) model. SAGE is an ensemble realization of multiple FAGR with each component FAGR operating on a randomly selected subset of features. Ameta classifier is applied to aggregate the outputs of component classifiers to yield an ensemble classification result. We performed extensive experimentations using three real-world HSI datasets, to compare the performance of FAGR and SAGE against several existing graph-based HSI classifiers. The experiment results show that the proposed SAGE achieves 95.78% classification accuracy on the Indian Pines dataset using limited labeled samples, out-performing existing models in terms of shorter execution time and better classification accuracy.