The open-vocabulary understanding of UAV aerial images plays a crucial role in enhancing the intelligence level of remote sensing applications, such as disaster assessment, precision agriculture, and urban planning. In this paper, we propose an innovative open-vocabulary model for UAV images, which combines vision–language methods to achieve efficient recognition and segmentation of unseen categories by generating multi-view image descriptions and feature extraction. To enhance the generalization ability and robustness of the model, we adopted Mixup technology to blend multiple UAV images, generating more diverse and representative training data. To address the limitations of existing open-vocabulary models in UAV image analysis, we leverage the GPT model to generate accurate and professional text descriptions of aerial images, ensuring contextual relevance and precision. The image encoder utilizes a U-Net with Mamba architecture to extract key point information through edge detection and partition pooling, further improving the effectiveness of feature representation. The text encoder employs a fine-tuned BERT model to convert text descriptions of UAV images into feature vectors. Three key loss functions were designed: Generalization Loss to balance old and new category scores, semantic segmentation loss to evaluate model performance on UAV image segmentation tasks, and Triplet Loss to enhance the model’s ability to distinguish features. The Comprehensive Loss Function integrates these terms to ensure robust performance in complex UAV segmentation tasks. Experimental results demonstrate that the proposed method has significant advantages in handling unseen categories and achieving high accuracy in UAV image segmentation tasks, showcasing its potential for practical applications in diverse aerial imagery scenarios.

Accurate classification of three-dimensional (3D) point clouds in real-world environments is often impeded by sensor noise, occlusions, and incomplete data. To overcome these challenges, we propose SMCNet, a robust multimodal framework for 3D point cloud classification. SMCNet combines multi-view projection and neural radiance fields (NeRFs) to generate high-fidelity 2D representations with enhanced texture realism, addressing occlusions and lighting inconsistencies effectively. The Mamba model is further refined within this framework by integrating a depth perception module to capture long-range point interactions and adopting a dual-channel structure to enhance point-wise feature extraction. Fine-tuning adapters for the CLIP and Mamba models are also introduced, significantly improving cross-domain adaptability. Additionally, an intelligent voting mechanism aggregates predictions from multiple viewpoints, ensuring enhanced classification robustness. Comprehensive experiments demonstrate that SMCNet achieves state-of-the-art performance, outperforming the PointNet++ baseline with a 0.5% improvement in mean overall accuracy (mOA) on ModelNet40 and a 7.9% improvement on ScanObjectNN. In corruption resistance, SMCNet reduces the mean corruption error (mCE) by 0.8% on ModelNet40-C and 3.6% on ScanObjectNN-C. These results highlight the effectiveness of SMCNet in tackling real-world classification scenarios with noisy and corrupted data.