With the swift advancement of deep learning technologies, single hyperspectral image (HSI) super-resolution (SR) algorithms have observed considerable progress. However, methods leveraging 2D convolutions often struggle to effectively extract ...

Hyperspectral image (HSI) denoising can be implemented in a low-dimensional spectral subspace to utilize the high correlations across different bands and reduce the imposed computational burden, and subspace learning is a key strategy for ...

The joint classification of hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) data can provide complementary information for each other, which has become a prominent topic in the field of remote sensing. Nevertheless, the common ...

• A novel graph-attention based multimodal fusion network is designed.
• A triple feature extraction backbone is designed for the joint HSI–LiDAR dataset.
• An undirected weighted graph is constructed, then fused by attention strategy.

Existing spectral super-resolution (SSR) methods have achieved satisfactory performance by designing complicated deep convolution neural networks (DCNNs) to extract spectral and spatial features. However, these methods ignore the fact that the ...

Fusion of high spatial resolution multispectral (HR MS) and low spatial resolution hyperspectral (LR HS) images has become a significant way to produce high spatial resolution hyperspectral (HR HS) images. Though many methods have exploited the ...

• We establish a fusion model to constrain the fused HR HS image by SNS and SBC priors.
• According to the inheritance of SNS and SBC, we design a graph embedding network.
• We present an adaptive graph construction module to model the ...

This paper introduces Sill-Rgan, a novel Generative Adversarial Network (GAN) designed to improve hyperspectral image (HSI) classification under varying lighting conditions. Sill-Rgan uniquely maps different light condition domains, enhancing ...

Hyperspectral and multispectral (HS–MS) image fusion aims to reconstruct high-resolution hyperspectral images from low-resolution hyperspectral images and high-resolution multispectral images. While convolutional neural networks have been widely ...

• We advance a novel hyperspectral and multispectral image fusion method through dual cross Transformer model.
• The proposed method can reciprocally inject the spectral and spatial information to the target hyperspectral image.
• It ...

Hyperspectral (HS) pansharpening aims to fuse a low spatial resolution HS image with a registered high spatial resolution panchromatic (PAN) image to generate the enhanced HS image with both high spatial and spectral resolution. Existing pansharpening ...

Domain adaptation (DA) has become a widely used technique for cross-scene hyperspectral image (HSI) classification. Most DA methods aim to learn a domain invariant subspace that reduce the domain discrepancy between source and target domains. ...

• A marginal distance constraint is utilized to reduce the domain shift.
• LRDA adopts L2,1-norm to eliminate the influence of inessential features.
• A ridge regression term is designed to improve the discriminative ability.
• LRDA ...

Evolutionary multiobjective optimization is vigorous but not efficient in solving the hyperspectral sparse unmixing problem, while most related algorithms suffer from high computational complexity. This limitation becomes more pronounced in ...

• Dynamic change of CAT activity of mold was used to warn AFB1 contamination of stored maize.
• The rapid increase of CAT activity preceded the rapid accumulation of AFB1 for 2–3 days.
• Linkage-warning models of CAT-AFB1 under different ...

Aflatoxin B1 (AFB1) is a metabolite of mold with high toxicity, the early warning is of significance to prevent stored maize from AFB1 contamination. Catalase (CAT) is a precursor production before the synthesis of AFB1, which has a significant ...

With the innovation of sensors, communication, computer and other technologies, remote sensing (RS) imaging methods show a diversification trend. At present, relying on a variety of earth observation platforms, more multi-source RS data can be ...

—Graph convolutional network (GCN) has attracted much attention in the field of hyperspectral image classification for its excellent feature representation and convolution on arbitrarily structured non-Euclidean data. However, most state-of-the-...

Hyperspectral images (HSIs) have been applied to a wide range of areas thanks to their high spectral resolutions. However, spatial resolutions of HSIs are inevitably compromised due to hardware limits. While HSIs can be enhanced by the super ...

The application of hyperspectral imaging with computer-aided technology has promising prospects, and achieving real-time, efficient, and non-destructive detection, especially for food and agricultural products, undoubtedly poses a great ...

• A valid approach for the assessment of agri-food products by hyperspectral images is proposed.
• The network is an integration of convolution and self-attention.
• Robustness of the accuracy by efficient use of hyperspectral image ...

Recently, deep unfolding networks (DUNs) have been applied to the fusion of low spatial resolution hyperspectral (LR HS) and high spatial resolution multispectral (HR MS) images and achieved satisfactory high spatial resolution ...

• An RPCA-based image fusion model is built and optimized to produce HR HS images.

In this study, an enhanced method and system for hyperspectral image classification are presented, based on deep cross-scene few-shot learning. This pertains to the domain of remote sensing image processing technology and addresses the prevalent issues ...

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample ...

Convolutional neural networks have been widely used in the field of hyperspectral image (HSI) classification due to their excellent ability to model local regions, and have achieved good classification performance. However, HSI classification ...

Although the high number of bands in hyperspectral remote sensing images increases their usefulness, it also causes some processing difficulty. In supervised classification, one problem is decreasing classification accuracy due to the ...

• A new fusion-based classification is proposed for hyperspectral images based on stochastic nature of metaheuristic methods.
• A fully automated classification system is developed to classify hyperspectral remote sensing images.
• Many ...