Hyperspectral image (HSI) classification is commonly influenced by convolution neural networks (CNNs). However, the large number of parameters and computational complexity associated with CNNs can limit their practical application, particularly when computing and storage resources are limited. To address this challenge, we propose a channel-layer-oriented lightweight network for HSI classification. Motivated by existing structures that typically set large channels and stack multiple layers, we give more optimal solutions strategically to further compress the model. For intralayer feature extraction, we develop a channel-oriented spectral–spatial module (COS2M), which introduces a dual-single-channel (DSC) 3-D convolution that works in conjunction with depthwise convolution to fully extract spectral–spatial information. For interlayer information transmission, we propose a novel neighbor-pixel-aware activation function (NPAF), where the activation of a single pixel is determined by the learnable interaction with its neighbor range that enhances information transmission and improves the network’s fitting ability through the single activation layer. By implementing these strategies, we aim to overcome the limitations of traditional CNNs and enable efficient HSI classification within resource-constrained environments. The whole network is designed to be a compact end-to-end structure. It achieves better classification performance than other deep learning methods and lightweight models, even with limited training samples. The network parameters, model complexity, and inference time also demonstrate significant superiority, as confirmed by experiments on three benchmark datasets. The source codes are available publicly at: https://github.com/AchunLee/CLOLN_TGRS

Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance. However, the availability of labeled data is limited due to the significant human resources and time costs of labeling hyperspectral data. Unsupervised learning for hyperspectral image classification has thus received increasing attention. In this paper, we propose a novel unsupervised framework based on a contrastive learning method and a transformer model for hyperspectral image classification. The experimental results prove that our model can efficiently extract hyperspectral image features in unsupervised situations.

Controllably tuned infrared emissivity has attracted great interest for potential application in adaptive thermal camouflage. In this work, we report a flexible multilayer graphene based infrared device on a porous polyethylene membrane, where the infrared emissivity could be tuned by ionic liquid intercalation. The Fermi level of surface multilayer graphene shifts to a high energy level through ionic liquid intercalation, which blocks electronic transition below the Fermi level. Thus, the optical absorptivity/emissivity of graphene could be controlled by intercalation. Experimentally, the infrared emissivity of surface graphene was found to be tuned from 0.57 to 0.41 after ionic liquid intercalation. Meanwhile, the relative reflectivity R_v/R₀ of surface graphene increased from 1.0 to 1.15. The strong fluorescence background of Raman spectra, the upshift of the G peak (~23 cm⁻¹), and the decrease of sheet resistance confirmed the successful intercalation of ionic liquid into the graphene layers. This intercalation control of the infrared emissivity of graphene in this work displays a new way of building an effective thermal camouflage system.

In practical hyperspectral image cross-scene classification (HSICC) tasks, the arduous work of obtaining labels and the distribution inconsistency caused by spectral shift leave deep learning methods to face great challenges. Unsupervised domain adaptation aims to exploit knowledge from the annotated source domain and transfer it to the unlabeled target domain, thereby boosting the performance of unsupervised classification. Nevertheless, existing HSICC approaches cannot effectively exploit class structure information from target data. In particular, this article proposes an unsupervised joint adversarial domain adaptation (UJADA) architecture for HSICC to further narrow the distribution gap between distinct domains. The proposed method contains two modules: domain adversarial module that learns domain-invariant features, biclassifier adversarial module that explores task-specific decision boundaries between classes, and both share a feature generator consisting of the dense-based spectral–spatial convolution network. The UJADA simultaneously considers domain- and class-level feature alignment between source and target hyperspectral images in unified adversarial learning processing. Furthermore, the classifier determinacy disparity metric is introduced to fine-grained measure the output probabilistic discrepancy between two task-specific label predictors on target data, thus ensuring the discriminability of transferable features. Comprehensive experiments and ablation studies conducted on two public cross-scene data pairs and our newly acquired ultralow-altitude hyperspectral images under different illumination conditions demonstrate the superior performance of the proposed algorithm, which will greatly promote the practical application of hyperspectral intelligent perception technology.

Hyperspectral image (HSI) clustering is a major challenge due to the redundant spectral information in HSIs. In this paper, we propose a novel deep subspace clustering method that extracts spatial–spectral features via contrastive learning. First, we construct positive and negative sample pairs through data augmentation. Then, the data pairs are projected into feature space using a CNN model. Contrastive learning is conducted by minimizing the distances of positive pairs and maximizing those of negative pairs. Finally, based on their features, spectral clustering is employed to obtain the final result. Experimental results gained over three HSI datasets demonstrate that our proposed method is superior to other state-of-the-art methods.

Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image classification are based on convolutional neural networks (CNN). Those methods require heavy GPU memory resources and run time. Recently, another deep learning model, the transformer, has been applied for image recognition, and the study result demonstrates the great potential of the transformer network for computer vision tasks. In this paper, we propose a model for hyperspectral image classification based on the transformer, which is widely used in natural language processing. Besides, we believe we are the first to combine the metric learning and the transformer model in hyperspectral image classification. Moreover, to improve the model classification performance when the available training samples are limited, we use the 1-D convolution and Mish activation function. The experimental results on three widely used hyperspectral image data sets demonstrate the proposed model’s advantages in accuracy, GPU memory cost, and running time.