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Abstract Although low‐rank representation (LRR)‐based subspace learning has been widely applied for feature extraction in computer vision, how to enhance the discriminability of the low‐dimensional features extracted by LRR based subspace learning methods is still a problem that needs to be further investigated. Therefore, this paper proposes a novel low‐rank preserving embedding regression (LRPER) method by integrating LRR, linear regression, and projection learning into a unified framework. In LRPER, LRR can reveal the underlying structure information to strengthen the robustness of projection learning. The robust metric L2,1‐norm is employed to measure the low‐rank reconstruction error and regression loss for moulding the noise and occlusions. An embedding regression is proposed to make full use of the prior information for improving the discriminability of the learned projection. In addition, an alternative iteration algorithm is designed to optimise the proposed model, and the computational complexity of the optimisation algorithm is briefly analysed. The convergence of the optimisation algorithm is theoretically and numerically studied. At last, extensive experiments on four types of image datasets are carried out to demonstrate the effectiveness of LRPER, and the experimental results demonstrate that LRPER performs better than some state‐of‐the‐art feature extraction methods.

Accurate and timely monitoring of pests is an effective way to minimize the negative effects of pests in agriculture. Since deep learning-based methods have achieved good performance in object detection, they have been successfully applied for pest detection and monitoring. However, the current pest detection methods fail to balance the relationship between computational cost and model accuracy. Therefore, this paper proposes a lightweight, locality-aware faster R-CNN (LLA-RCNN) method for effective pest detection and real-time monitoring. The proposed model uses MobileNetV3 to replace the original backbone, reduce the computational complexity, and compress the size of the model to speed up pest detection. The coordinate attention (CA) blocks are utilized to enhance the locality information for highlighting the objects under complex backgrounds. Furthermore, the generalized intersection over union (GIoU) loss function and region of interest align (RoI Align) technology are used to improve pest detection accuracy. The experimental results on different types of datasets validate that the proposed model not only significantly reduces the number of parameters and floating-point operations (FLOPs), but also achieves better performance than some popular pest detection methods. This demonstrates strong generalization capabilities and provides a feasible method for pest detection on resource-constrained devices.

Hyperspectral images (HSIs) capture a wide range of spectral features across multiple bands of light, from visible to near-infrared. Hyperspectral image classification technology enables researchers to accurately identify and analyze the composition and distribution of surface materials. Current mainstream deep learning methods typically use block sampling to capture spatial features for the model. However, this approach can affect classification results due to the influence of neighboring features within the sample block. To improve the model’s focus on the center of the sampling block, this study proposes a center highlight with multiscale CNN for hyperspectral image classification (CHMSC). The network utilizes an automatic channel selector (Auto-CHS) to fully consider every channel feature and capture the correlation between the channels. Then, CHMSC enhances the model’s ability to concentrate on the central features of the sampling block utilizing structures such as the center highlight. Finally, before outputting the prediction results, an SENet is employed to further refine the features and learn associate interactions between different scales of spatial features and spectral features. Experimental results from three hyperspectral datasets validate the effectiveness of the proposed method. Specifically, when 15 samples from each class are selected for training, CHMSC achieves the highest overall accuracy (OA) of 90.05%, 92.78%, and 90.15% on the three datasets, outperforming other methods with increases of more than 3.11%, 1.8%, and 2.01% in OA, respectively.

Low-rank representation (LRR) is widely utilized in image feature extraction, as it can reveal the underlying correlation structure of data. However, the subspace learning methods based on LRR suffer from the problems of lacking robustness and discriminability. To address these issues, this paper proposes a new robust feature extraction method named the weighted Schatten p-norm minimization via low-rank discriminative embedding regression (WSNM-LRDER) method. This method works by integrating weighted Schatten p-norm and linear embedding regression into the LRR model. In WSNM-LRDER, the weighted Schatten p-norm is adopted to relax the low-rank function, which can discover the underlying structural information of the image, to enhance the robustness of projection learning. In order to improve the discriminability of the learned projection, an embedding regression regularization is constructed to make full use of prior information. The experimental results on three hyperspectral images datasets show that the proposed WSNM-LRDER achieves better performance than some advanced feature extraction methods. In particular, the proposed method yielded increases of more than 1.2%, 1.1%, and 2% in the overall accuracy (OA) for the Kennedy Space Center, Salinas, and Houston datasets, respectively, when comparing with the comparative methods.

Abstract Recently, relaxation linear regression has received increasing attention in image analysis. However, the current relaxation linear regression methods fail to consider the local geometrical structure and spatial information while they are applied for hyperspectral image (HSI) classification. To address the above problems, this letter proposes a novel relaxation linear regression with spectral–spatial constrained locality adaptive regularization (SSLA‐RLR) method for HSI classification. The SSLA‐RLR method not only integrates the locality adaptive graph with relaxation linear regression to adaptively exploit the local geometrical structure for relieving the side effects of noise corruptions, but also takes the spatial correlations between each data point and its neighbours into consideration via spectral–spatial constraint. Finally, extensive experiments are conducted on three benchmark HSI datasets to demonstrate the effectiveness of the proposed method.

Rice has an important position in China as well as in the world. With the wide application of rice hybridization technology, the problem of mixing between individual varieties has become more and more prominent, so the variety identification of rice is important for the agricultural production, the phenotype collection, and the scientific breeding. Traditional identification methods are highly subjective and time-consuming. To address this issue, we propose a novel locality preserved selective projection learning (LPSPL) method for non-destructive rice variety identification based on leaf hyperspectral characteristics. The proposed LPSPL method can select the most discriminative spectral features from the leaf hyperspectral characteristics of rice, which is helpful to distinguish different rice varieties. In the experiments, support vector machine (SVM) is adopted to conduct the rice variety identification based on the selected spectral features. The experimental results show that the proposed method here achieves higher identification rates, 96% for the early rice and 98% for the late rice, respectively, which are superior to some state-of-the-art methods.