Although linear discriminant analysis (LDA)-based subspace learning has been widely applied to hyperspectral image (HSI) classification, the existing LDA-based subspace learning methods exhibit several limitations: (1) They are often sensitive to noise and demonstrate weak robustness; (2) these methods ignore the local information inherent in data; and (3) the number of extracted features is restricted by the number of classes. To address these drawbacks, this paper proposes a novel joint sparse local linear discriminant analysis (JSLLDA) method by integrating embedding regression and locality-preserving regularization into the LDA model for feature dimensionality reduction of HSIs. In JSLLDA, a row-sparse projection matrix can be learned, to uncover the joint sparse structure information of data by imposing a $L_{2,1}$-norm constraint. The $L_{2,1}$-norm is also employed to measure the embedding regression reconstruction error, thereby mitigating the effects of noise and occlusions. A locality preservation term is incorporated to fully leverage the local geometric structural information of the data, enhancing the discriminability of the learned projection. Furthermore, an orthogonal matrix is introduced to alleviate the limitation on the number of acquired features. Finally, extensive experiments conducted on three hyperspectral image (HSI) datasets demonstrated that the performance of JSLLDA surpassed that of some related state-of-the-art dimensionality reduction methods.

Due to the low storage cost and high computation efficiency of hashing, cross-modal hashing has been attracting widespread attention in recent years. In this paper, we investigate how supervised cross-modal hashing (CMH) benefits from multi-label and contrastive learning (CL) by overcoming the following two challenges: (i) how to combine multi-label and supervised contrastive learning to consider diverse relationships among cross-modal instances, and (ii) how to reduce the sparsity of multi-label representation so as to improve the similarity measurement accuracy. To this end, we propose a novel cross-modal hashing framework, dubbed Multi-Label Weighted Contrastive Hashing (MLWCH). This framework involves compact consistent similarity representation, a new designed multi-label similarity calculation method that efficiently reduces the sparsity of multi-label by reducing redundant zero elements. Furthermore, a novel multi-label weighted contrastive learning strategy is developed to significantly improve hashing learning by assigning similarity weight to positive samples under both linear and non-linear similarities. Extensive experiments and ablation analysis over three benchmark datasets validate the superiority of our MLWCH method, especially over several outstanding baselines.

Unsupervised cross-modal hashing is a topic of considerable interest due to its advantages in terms of low storage costs and fast retrieval speed. Despite the impressive achievements of existing solutions, two challenges remain unaddressed: (1) Semantic similarity obtained without supervision is not accurate enough, and (2) the preservation of similarity structures lacks effectiveness due to the neglect of both global and local similarity. This paper introduces a new method, Multi-Grained Similarity Preserving and Updating (MGSPU), to tackle these challenges. To overcome the first challenge, MGSPU employs a newly designed strategy to update the semantic similarity matrix, effectively generating a high-confidence similarity matrix by eliminating noise in the original cross-modal features. For the second challenge, a novel multi-grained similarity preserving method is proposed, aiming to enhance cross-modal hash code learning by learning consistency in multi-grained similarity structures. Comprehensive experiments on two widely used datasets with nine state-of-the-art competitors validate the superior performance of our method in cross-modal hashing.

Image super-resolution has experienced significant advancements with the emergence of deep learning technology. However, deploying highly complex super-resolution networks on resource-constrained devices poses a challenge due to their substantial computational requirements. This paper presents the Adaptive Dynamic Shuffle Convolutional Parallel Network (ADSCPN), a novel lightweight super-resolution model designed to achieve an optimal balance between computational efficiency and image reconstruction quality. The ADSCPN framework employs large-kernel parallel depthwise separable convolutions, dynamic convolutions, and an enhanced attention mechanism to optimize feature extraction and improve detail preservation. Extensive evaluations on standard benchmark datasets demonstrate that ADSCPN achieves state-of-the-art performance while significantly reducing computational complexity, making it well-suited for practical applications on devices with limited computational resources.

Rice plays a pivotal role in global food security, particularly for Asian populations. However, its production is significantly threatened by insect pests, with Chilo suppressalis being a major pest in Asian rice-growing regions. Therefore, developing accurate predictive models for C. suppressalis outbreaks is essential. This study presents a novel time series forecasting model (named ESD-TripleStream) for C. suppressalis population dynamics based on a multi-stream structure, which addresses the limitations of existing approaches, which often omit the further decomposability of and the timestamp information in the time series. This model integrates Exponential Smoothing Decomposition (ESD) to separate the trend and seasonal components of time series data, along with a temporal feature stream to form a three-stream network to capture multi-scale periodic patterns and temporal dependencies. For our evaluation, we collected and constructed a novel dataset, referred to as HNRP-6R, which includes rice pest monitoring data from the past two decades (2000–2022) alongside 13 meteorological factors across six key rice producing regions in Hunan Province, southern China. ESD-TripleStream was evaluated across short-term and medium-term C. suppressalis population prediction scales using HNRP-6R, demonstrating state-of-the-art performance. Specifically, in short-term prediction, ESD-TripleStream achieved a 31.8% reduction in Mean Squared Error (MSE) and 26.55% reduction in Mean Absolute Error (MAE) compared to the PatchMLP model, while outperforming the transformer-based TimeXer by 14.43% in MSE and 9.8% in MAE. For medium-term prediction, ESD-TripleStream has both MSE and MAE significantly lower than those of baseline models such as P-sLSTM and xPatch. Furthermore, generalization tests on Nilaparvata lugens (N. lugens) population prediction demonstrated the model’s adaptability to diverse pest dynamics.