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.

Accurately obtaining both the number and the location of rice plants plays a critical role in agricultural applications, such as precision fertilization and yield prediction. With the rapid development of deep learning, numerous models for plant counting have been proposed. However, many of these models contain a large number of parameters, making them unsuitable for deployment in agricultural settings with limited computational resources. To address this challenge, we propose a novel pruning method, Cosine Norm Fusion (CNF), and a lightweight feature fusion technique, the Depth Attention Fusion Module (DAFM). Based on these innovations, we modify the existing P2PNet network to create P2P-CNF, a lightweight model for rice plant counting. The process begins with pruning the trained network using CNF, followed by the integration of our lightweight feature fusion module, DAFM. To validate the effectiveness of our method, we conducted experiments using rice datasets, including the RSC-UAV dataset, captured by UAV. The results demonstrate that our method achieves a MAE of 3.12 and an RMSE of 4.12 while utilizing only 33% of the original network parameters. We also evaluated our method on other plant counting datasets, and the results show that our method achieves a high counting accuracy while maintaining a lightweight architecture.

Image denoising remains a fundamental challenge in digital image processing due to the inevitable presence of noise during image acquisition and transmission. While existing noise filtering methods predominantly focus on local spatial information, they often overlook crucial structural information from other perspectives, such as local manifold and global structures. To address this limitation, we propose a novel linear projection-based noise filtering (LPNF) framework grounded in linear projection learning theory. This framework innovatively learns a linear projection for noise filtering by incorporating multiple structural information sources - local spatial, local manifold, and global structures - through well-defined criteria. We present two specialized implementations of the LPNF framework: PCA-based LPNF (LPNF-PCA) and LPP-based LPNF (LPNF-LPP). The LPNF-PCA simultaneously leverages local spatial and global information, while LPNF-LPP integrates both local manifold and spatial information for enhanced denoising performance. Comprehensive experiments conducted on four standard test images with various noise types demonstrate that both LPNF-PCA and LPNF-LPP consistently outperform state-of-the-art denoising methods in terms of both quantitative.