A novel spectral–spatial difference convolution network (S $^{2}$ DCN) is proposed for hyperspectral image (HSI) classification, which integrates the difference principle into the deep learning framework. S $^{2}$ DCN employs a learnable gradient encoding pattern to extract important detail features in spectral and spatial domains, alleviating the information loss caused by the oversmoothing effect in deep feature extraction. Specifically, the feature extraction modules in S $^{2}$ DCN are designed, namely spectral difference convolution (SeDC) module and spatial difference convolution (SaDC) module. The SeDC module performs 1-D difference convolution in the spectral domain to capture peak-valley information in sensitive narrow bands, enhance subtle spectral differences, and preserve fine-grained features. The SaDC module employs 2-D difference convolution in the spatial domain, integrating fine-structural features while preserving the deep abstract features extracted by vanilla convolutions. This further empowers the capability of the model to extract discriminative features. A series of experiments are performed on four publicly available HSI datasets to demonstrate the effectiveness of S $^{2}$ DCN method, which is compared with current state-of-the-art models. The experimental results show that the proposed S $^{2}$ DCN outperforms competitors and achieves optimal classification performance.

A Sequence-Variable Attention Temporal Convolutional Network (SVA-TCN) is proposed for lithology classification based on well log data. This study aims to address the issue that native TCN pays insufficient attention to crucial logging variables and sequence structural features in well log tasks. A novel Sequence-Variable Attention Mechanism module is designed to effectively extract features by adding the bidirectional attention mechanism, which comprises the sequence attention mechanism and the variable attention mechanism. The sequence attention mechanism, acting along the depth dimension, focuses on modeling the multiscale sequence features. The variable attention mechanism, working along the logging variable dimension, contributes to learning the importance of different logging variables and exploring the internal correlations among them. To verify the validity of the model, experiments are conducted in the volcanic reservoir of the Yingcheng Formation in the Xujiaweizi Depression of the Songliao Basin. Seven logging variables sensitive to lithological changes are selected, including compressional wave slowness, density, and compensated neutron logging, etc., to construct a lithology identification model using SVA-TCN. Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. The findings of this study indicate that the SVA-TCN model is well-suited for lithology identification in volcanic reservoirs. It provides more reliable prediction results and exhibits good stability and generalization, offering a new avenue to volcanic lithology identification.

Substantial deep learning methods have been utilized for hyperspectral image (HSI) classification recently. Vision Transformer (ViT) is skilled in modeling the overall structure of images and has been introduced to HSI classification task. However, the fixed patch division operation in ViT may lead to insufficient feature extraction, especially the features of the edges between patches will be ignored. To address this problem, we devise a workflow for HSI classification based on the Nested Transformers (NesT). The NesT employs the block aggregation module to extract edge information between patches, which realizes cross-block communication of nonlocal information and optimizes global information extraction. In this paper, the NesT is used for HSI classification for the first time. The experiments are carried out on four widely used hyperspectral datasets: Indian Pines, Salinas, Tea Farm, and Xiongan New Area (Matiwan Village). The obtained results reveal that the NesT can provide competitive results compared to conventional machine learning and deep learning methods and achieve top accuracy on four datasets, which proves the superiority of the NesT in HSI classification with limited training samples.