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Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance. [ABSTRACT FROM AUTHOR]

Despite notable advancements achieved on Hyperspectral (HS) pansharpening tasks through deep learning techniques, previous methods are inherently constrained by convolution or self-attention intrinsic defects, leading to limited performance. In this paper, we proposed an Attention-Interactive Dual-Branch Convolutional Neural Network (AIDB-Net) for HS pansharpening. Our model purely consists of convolutional layers and simultaneously inherits the strengths of both convolution and self-attention, especially the modeling of short- and long-range dependencies. Specially, we first extract, tokenize, and align the hyperspectral image (HSI) and panchromatic image (PAN) by Overlapping Patch Embedding Blocks. Then, we specialize a novel Spectral-Spatial Interactive Attention which is able to globally interact and fuse the cross-modality features. The resultant token-global similarity scores can guide the refinement and renewal of the textural details and spectral characteristics within HSI features. By deeply combined these two paradigms, our AIDB-Net significantly improve the pansharpening performance. Moreover, with the acceleration by the convolution inductive bias, our interactive attention can be trained without large scale dataset and achieves competitive time cost with its counterparts. Compared with the state-of-the-art methods, our AIDB-Net makes 5.2%, 3.1%, and 2.2% improvement on PSNR metric on three public datasets, respectively. Comprehensive experiments quantitatively and qualitatively demonstrate the effectiveness and superiority of our AIDB-Net. [ABSTRACT FROM AUTHOR]

There have been considerable efforts in generating tree crown maps from satellite images. However, tree localization in urban environments using satellite imagery remains a challenging task. One of the difficulties in complex urban tree detection tasks lies in the segmentation of dense tree crowns. Currently, methods based on semantic segmentation algorithms have made significant progress. We propose to split the tree localization problem into two parts, dense clusters and single trees, and combine the target detection method with a procedural generation method based on planting rules for the complex urban tree detection task, which improves the accuracy of single tree detection. Specifically, we propose a two-stage urban tree localization pipeline that leverages deep learning and planting strategy algorithms along with region discrimination methods. This approach ensures the precise localization of individual trees while also facilitating distribution inference within dense tree canopies. Additionally, our method estimates the radius and height of trees, which provides significant advantages for three-dimensional reconstruction tasks from remote sensing images. We compare our results with other existing methods, achieving an 82.3% accuracy in individual tree localization. This method can be seamlessly integrated with the three-dimensional reconstruction of urban trees. We visualized the three-dimensional reconstruction of urban trees generated by this method, which demonstrates the diversity of tree heights and provides a more realistic solution for tree distribution generation. [ABSTRACT FROM AUTHOR]