While semisupervised methods based on graph convolutional networks (GCNs) can achieve good results in hyperspectral image (HSI) classification, their performance is limited as they rely only on spatial-spectral similarity when constructing local adjacency graphs. Moreover, relying on local adjacency graphs can limit the ability of the methods to ensure the consistency of global feature in complex hyperspectral remote sensing environments. Using the spatial-spectral information is typically not sufficient for providing a reliable similarity measurement to construct a global graph when the spectral variability is large and the spatial distance is long in intraclass pixels. To address this issue, this article presents a novel globally consistent GCN (GCGCN) for HSI classification. According to the proposed method, a local reliable initial graph is first built from inherent spatial-spectral information by considering this graph as a variable to optimize. Then, adaptive global high-order neighbors are explored to capture the underlying rich spatial contextual information by utilizing the graph topological consistent connectivity instead of the common strategy of using only the spatial-spectral similarity measurement. Finally, the adaptive global high-order graph structure and two-layer networks are combined to achieve the global feature smoothing of the same class samples and maintain high global feature consistency. The proposed GCGCN method is evaluated on three real HSI data sets to demonstrate its superiority compared to ten different classification methods. Moreover, the proposed GCGCN method achieves state-of-the-art classification results on three data sets in terms of four classification evaluation metrics, including overall accuracy (OA), kappa coefficient (KC), average accuracy (AA), and class accuracy (CA).

Compressing hyperspectral images (HSIs) into compact representations under the premise of ensuring high-quality reconstruction is an essential task in hyperspectral image processing. However, existing compression methods usually encode images by smoothing due to the low-frequency information occupying a prominent component in most images. Consequently, these methods fail to capture sufficient structural information, especially in low bit rates, often causing inferior reconstruction. To address this problem, we propose herein an edge-guided hyperspectral compression network, called CENet, to realize high-quality reconstruction. To enhance the structural latent representation ability, the CENet model incorporates an edge extractor neural network into the compression architecture to guide compression optimization by the edge-guided loss. We propose an interactive dual attention module to selectively learn edge features, obtain the most effective edge structure, and avoid additional edge information redundancy at the same time. In the proposed CENet, the edge-guided loss and interactive dual attention module are combined to enhance the comprehensive structure of the latent representation. Concretely, interactive dual attention makes the edge extraction network focus only on moderate boundaries rather than on all edges, which enables savings on the bit rate cost and helps achieve a strong structural representation. As a result, the reconstruction quality is significantly improved. The extensive experiments on seven HSI datasets verify that our model can effectively raise the rate-distortion performance for HSIs of any type or resolution (e.g., yielding an average PSNR of 30.59 dB at 0.2382 bpppb, which exceeds the baseline for Chikusei by 10.99 %).

In recent years, advances in deep learning have greatly promoted the development of hyperspectral image (HSI) compression algorithms. However, most existing compression approaches directly rely on rate-distortion optimization without other guidance during model learning. Therefore, this brings challenges to distinguishing similar features or objects that are widely available in HSIs, especially in remote sensing scenes, since quantification in lossy compression can cause informative attribute (e.g., category) collapse and loss problems at high compression ratios. In this paper, we propose a novel hyperspectral compression network via contrastive learning (HCCNet) to help generate discriminative representations and preserve informative attributes as much as possible. Specifically, we design a contrastive informative feature encoding (CIFE) to extract and organize discriminative attributes from the original HSIs by enlarging the discrimination over the learned latents in different channel indexes to relieve attribute collapses. In the case of attribute losses, we define a contrastive invariant feature recovery (CIFR) to discover the lost attributes via contrastive feature refinement. Experiments on five different HSI datasets illustrate that the proposed HCCNet can achieve impressive compression performance, such as improvement of the peak signal-to-noise ratio (PSNR) from 28.86 dB (at 0.2284 bpppb) to 30.30 dB (at 0.1960 bpppb) on the Chikusei dataset.

Hyperspectral compression is one of the most common techniques in hyperspectral image processing. Most recent learned image compression methods have exhibited excellent rate-distortion performance for natural images, but they have not been fully explored for hyperspectral compression tasks. In this paper, we propose a trainable network architecture for hyperspectral compression tasks, which not only considers the anisotropic characteristic of hyperspectral images but also embeds an accurate entropy model using the non-Gaussian prior knowledge of hyperspectral images and nonlinear transform. Specifically, we first design a spatial-spectral block, involving a spatial net and a spectral net as the base components of the core autoencoder, which is more consistent with the anisotropic hyperspectral cubes than the existing compression methods based on deep learning. Then, we design a Student’s T hyperprior that merges the statistics of the latents and the side information concepts into a unified neural network to provide an accurate entropy model used for entropy coding. This not only remarkably enhances the flexibility of the entropy model by adjusting various values of the degree of freedom, but also leads to a superior rate-distortion performance. The results illustrate that the proposed compression scheme supersedes the Gaussian hyperprior universally for virtually all learned natural image codecs and the optimal linear transform coding methods for hyperspectral compression. Specifically, the proposed method provides a 1.51% to 59.95% average increase in peak signal-to-noise ratio, a 0.17% to 18.17% average increase in the structural similarity index metric and a 6.15% to 64.60% average reduction in spectral angle mapping over three public hyperspectral datasets compared to the Gaussian hyperprior and the optimal linear transform coding methods.

An innovative multi-attribute decision making (MADM) approach is put forward in this paper, which is demonstrated to be more precise than other methods currently available, especially, when the evaluations given by the decision maker(s) involve the complexity of time dependent and interdependent data. As for dynamic MADM interval-valued intuitionistic fuzzy environments with the time and attribute weights completely unknown, the new approach achieves the precision improvement by overcoming the drawbacks, the traditional geometric average operator existing: these fail to consider the relationships of the integrated data as well as the influences of attribute weights and time preference factor. The new method is systematically defined in a seven step approach; steps within the approach consist of the definition of multiperiod power-weighted geometric average operators and relative optimization models to compute attribute weights and time weights. A case analysis on the option of a superior company about venture capital funding is provided to demonstrate the method proposed; validation studies present a quantitative cross-comparison study and a sensitivity analysis of the new approach.

Cervical cancer, a prevalent malignancy among women, demands early screening and diagnosis for improved cure rates. Computer-aided screening systems offer accurate and rapid detection, reducing errors and enhancing efficiency. Convolutional neural networks have been extensively employed for cervical cancer cell detection in TCT images. However, these methods often overlook cervical cell morphology and intricate TCT image backgrounds, resulting in biased models. Moreover, their complexity limits practical application efficiency. Nevertheless, the causal attention mechanism can confine the model’s focus solely to the current morphology, preventing interference from other factors and effectively mitigating bias. And, lightweight strategies can enhance model efficiency and performance by reducing parameter count and computational demands. Thus, we introduce a lightweight high-performance model based on causal attention. Our cervical cancer cell detection model integrates causal attention-guided deformable convolutions in its backbone. This novel backbone improves CNN’s capacity to extract cellular morphological features, alleviating bias from target-background correlation. Additionally, a lightweight GSConv structure streamlines the neck architecture, further boosting efficiency. We also introduce a Grad-CAM-based explanation method for swift insight into model predictions. Experimental results demonstrate heightened precision, reducing parameters and computation by 20%, with a 1.9% average mAP increase. Our method reduces complexity and enhances precision in cervical cancer cell detection, showcasing its efficacy and potential in medical image object detection.