Ruyi Feng

Renxiang Guan, Zihao Li, Xianju Li et al.

High-dimensional and complex spectral structures make the clustering of hyperspectral images (HSI) a challenging task. Subspace clustering is an effective approach for addressing this problem. However, current subspace clustering algorithms are primarily designed for a single view and do not fully exploit the spatial or textural feature information in HSI. In this study, contrastive multi-view subspace clustering of HSI was proposed based on graph convolutional networks. Pixel neighbor textural and spatial-spectral information were sent to construct two graph convolutional subspaces to learn their affinity matrices. To maximize the interaction between different views, a contrastive learning algorithm was introduced to promote the consistency of positive samples and assist the model in extracting robust features. An attention-based fusion module was used to adaptively integrate these affinity matrices, constructing a more discriminative affinity matrix. The model was evaluated using four popular HSI datasets: Indian Pines, Pavia University, Houston, and Xu Zhou. It achieved overall accuracies of 97.61%, 96.69%, 87.21%, and 97.65%, respectively, and significantly outperformed state-of-the-art clustering methods. In conclusion, the proposed model effectively improves the clustering accuracy of HSI.

Renxiang Guan, Zihao Li, Chujia Song et al.

Spatial correlations between different ground objects are an important feature of mining land cover research. Graph Convolutional Networks (GCNs) can effectively capture such spatial feature representations and have demonstrated promising results in performing hyperspectral imagery (HSI) classification tasks of complex land. However, the existing GCN-based HSI classification methods are prone to interference from redundant information when extracting complex features. To classify complex scenes more effectively, this study proposes a novel spatial-spectral reliable contrastive graph convolutional classification framework named S2RC-GCN. Specifically, we fused the spectral and spatial features extracted by the 1D- and 2D-encoder, and the 2D-encoder includes an attention model to automatically extract important information. We then leveraged the fused high-level features to construct graphs and fed the resulting graphs into the GCNs to determine more effective graph representations. Furthermore, a novel reliable contrastive graph convolution was proposed for reliable contrastive learning to learn and fuse robust features. Finally, to test the performance of the model on complex object classification, we used imagery taken by Gaofen-5 in the Jiang Xia area to construct complex land cover datasets. The test results show that compared with other models, our model achieved the best results and effectively improved the classification performance of complex remote sensing imagery.

Yan Ding, Hao Cheng, Ziliang Ye et al.

We propose Adjustable Molecular Representation (AdaMR), a new large-scale uniform pre-training strategy for small-molecule drugs, as a novel unified pre-training strategy. AdaMR utilizes a granularity-adjustable molecular encoding strategy, which is accomplished through a pre-training job termed molecular canonicalization, setting it apart from recent large-scale molecular models. This adaptability in granularity enriches the model's learning capability at multiple levels and improves its performance in multi-task scenarios. Specifically, the substructure-level molecular representation preserves information about specific atom groups or arrangements, influencing chemical properties and functionalities. This proves advantageous for tasks such as property prediction. Simultaneously, the atomic-level representation, combined with generative molecular canonicalization pre-training tasks, enhances validity, novelty, and uniqueness in generative tasks. All of these features work together to give AdaMR outstanding performance on a range of downstream tasks. We fine-tuned our proposed pre-trained model on six molecular property prediction tasks (MoleculeNet datasets) and two generative tasks (ZINC250K datasets), achieving state-of-the-art (SOTA) results on five out of eight tasks.