Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. However, SLGDA fails to exploit the spatial information. To address this problem, a tensor sparse and low-rank graph-based discriminant analysis (TSLGDA) is proposed in this paper. By regarding the hyperspectral data cube as a third-order tensor, small local patches centered at the training samples are extracted for the TSLGDA framework to maintain the structural information, resulting in a more discriminative graph. Subsequently, dimensionality reduction is performed on the tensorial training and testing samples to reduce data redundancy. Experimental results of three real-world hyperspectral datasets demonstrate that the proposed TSLGDA algorithm greatly improves the classification performance in the low-dimensional space when compared to state-of-the-art DR methods.

Recurrent neural networks, especially the convolutional long short-term memory (ConvLSTM), have attracted plenty of attention and shown promising results due to their ability in modeling long-term dependencies in many research fields. In this paper, a lightweight tensor attention-driven ConvLSTM neural network (TACLNN) is proposed for hyperspectral image (HSI) classification. Firstly, to reduce the trainable parameters and memory requirements of ConvLSTM (specifically, the 2-D version of LSTM, i.e., ConvLSTM2D), a lightweight ConvLSTM2D cell is developed by utilizing tensor-train decomposition, resulting in a TT-ConvLSTM2D cell, with which a spatial-spectral TT-ConvLSTM 2-D neural network (SSTTCL2DNN) is built. However, it is inevitable for SSTTCL2DNN to obtain lower accuracies for HSI classification. To recover the accuracy loss caused by the TT-ConvLSTM2D cell in SSTTCL2DNN, a learnable tensor attention residual block (TARB) module is built to further enhance its geometrical structure. When applied to three widely used HSI benchmarks, the proposed TACLNN model outperforms several state-of-the-art methods for HSI classification. In addition, the proposed TACLNN can effectively reduce the number of parameters and storage requirements achieving higher classification accuracies as compared to other competitive baselines.

The curse of dimensionality and noise corruption are two tough problems that need to be solved in hyperspectral image (HSI) classification. However, the current feature dimensionality reduction methods, including both feature extraction and feature selection ones, cannot simultaneously solve the above two problems well. To address this issue, this paper proposes a novel method called L _2,p -norm-based robust embedding regression ( L _2,p -RER) for robust feature dimensionality reduction of HSI, which can effectively suppress the impact of noises and reduce the feature dimensions. Specifically, L _2,p -RER first integrates projection learning with robust principle component analysis (RPCA) to remove noise in a low-dimensional space. Secondly, an embedding regression regularization is proposed to improve the discriminability of the extracted low-dimensional features. Thirdly, a L _2,1 -norm constraint is imposed to improve the interpretability of the learned projection matrix, which can jointly extract the key features from all bands with their physical meanings certainly preserved. Last but most important, the L _2,p -norm that can adaptively balance the sparsity and the convexity is employed to model the noise and regression residual in the embedded low-dimensional space, which can further enhance the robustness and generalization of the proposed method. In addition, extensive experiments conducted on three benchmark HSI datasets validated the effectiveness of the proposed method.

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.

Subspace learning has been widely applied for feature extraction of hyperspectral images (HSIs) and achieved great success. However, the current methods still leave two problems that need to be further investigated. Firstly, those methods mainly focus on finding one or multiple projection matrices for mapping the high-dimensional data into a low-dimensional subspace, which can only capture information from each direction of high-order hyperspectral data separately. Secondly, the feature extraction performance is barely satisfactory when HSI data is severely corrupted by noise. To address these issues, this paper presents a t-linear tensor subspace learning (tLTSL) model for robust feature extraction of HSIs based on t-product projection. In the model, t-product projection is a new defined tensor transformation way similar to linear transformation in vector space, which can maximally capture the intrinsic structure of tensor data. The integrated tensor low-rank and sparse decomposition can effectively remove the noise corruption and the learned t-product projection can directly transform the high-order HSI data into a subspace with information from all modes comprehensively considered. Moreover, a proposition related to tensor rank is proofed for interpreting the meaning of the tLTSL model. Extensive experiments are conducted on two different kinds of noise ( i.e ., simulated and real noise) corrupted HSI data, which validate the effectiveness of tLTSL.

Linear regression and its variants have achieved considerable success in image classification. However, those methods still encounter two challenges when dealing with hyperspectral image (HSI) classification. On the one hand, the existing ones focus on mining the relationship between the label space and original data space during the classifier training, which is generally sensitive to noise corruptions. On the other hand, transforming the training samples into a strict binary label matrix makes the generalization ability of the classifier limited. To address these challenges, this article constructs a novel integrative model called tensor decomposition-based relaxed linear regression (TDRLR) for HSI classification. First, the model adopts tensor canonical polyadic (CP) decomposition to learn two dictionaries from spatial and spectral directions, respectively, which can help to generate a double dictionary representation for HSI data. Then, the linear regression classifier is integrated to learn a transformation that reveals the mapping relation between the double dictionary representation and label space rather than the original data for enhancing robustness. Meanwhile, a more flexible way, label relaxation (LR), is employed to enlarge the margins between different classes. More importantly, the learned double dictionary representation and classifier can be fine-tuned in tandem to enhance performance through the designed alternate iterative joint learning algorithm. Experiments conducted on four real-world HSI datasets demonstrate that the proposed method achieves significant improvements in classification performance with a small size training set, when compared with state-of-the-art HSI classification methods.