Transformer has been widely applied in image processing tasks as a substitute for Convolutional Neural Networks (CNNs) for feature extraction due to its superiority in global context modeling and flexibility in model generalization. However, the existing transformer-based methods for semantic segmentation of Remote Sensing (RS) images are still with several limitations, which can be summarized into two main aspects: 1) the transformer encoder is generally combined with CNN-based decoder, leading to inconsistency in feature representations; 2) the strategies for global and local context information utilization are not sufficiently effective. Therefore, in this paper, a Global-Local Transformer Segmentor (GLOTS) framework is proposed for semantic segmentation of RS images to acquire consistent feature representations by adopting transformers for both encoding and decoding, in which a Masked Image Modeling (MIM) pretrained transformer encoder is adopted to learn semantic-rich representations of input images, and a multi-scale global-local transformer decoder is designed to fully exploit the global and local features. Specifically, the transformer decoder uses a feature separation-aggregation module (FSAM) to utilize the feature adequately at different scales and adopts a global-local attention module (GLAM) containing Global Attention Block (GAB) and Local Attention Block (LAB) to capture the global and local context information respectively. Furthermore, a Learnable Progressive Upsampling Strategy (LPUS) is proposed to restore the resolution progressively, which can flexibly recover the fine-grained details in the upsampling process. Experimental results on the three benchmark RS datasets demonstrate that the proposed GLOTS is capable of achieving better performance with some state-of-the-art methods, and the superiority of the proposed framework is also verified by ablation studies. The code will be available at https://github.com/lyhnsn/GLOTS.

Hyperspectral videos (HSVs) can record more adequate detail clues than other videos, which is especially beneficial in cases of abundant spectral information. Although traditional methods based on correlation filters (CFs) employed to explore spectral information locally achieve promising results, their performances are limited by ignoring global information. In this paper, a joint spectral–spatial information method, named spectral–spatial transformer-based feature fusion tracker (SSTFT), is proposed for hyperspectral video tracking, which is capable of utilizing spectral–spatial features and considering global interactions. Specifically, the feature extraction module employs two parallel branches to extract multiple-level coarse-grained and fine-grained spectral–spatial features, which are fused with adaptive weights. The extracted features are further fused with the context fusion module based on a transformer with the hyperspectral self-attention (HSA) and hyperspectral cross-attention (HCA), which are designed to capture the self-context feature interaction and the cross-context feature interaction, respectively. Furthermore, an adaptive dynamic template updating strategy is used to update the template bounding box based on the prediction score. The extensive experimental results on benchmark hyperspectral video tracking datasets demonstrated that the proposed SSTFT outperforms the state-of-the-art methods in both precision and speed.

Transformer architecture has demonstrated significant potential in hyperspectral object tracking by leveraging global correlation learning to accurately represent the data distribution. However, existing hyperspectral object trackers based on transformer models typically rely on costly pre-trained models, making them prone to crashing due to overfitting when tuned on small-scale hyperspectral videos, greatly limiting their performance. To address this challenge, in this paper, a Hybrid Transformer with Adaptive Content and Position Embedding (HTACPE) tracker is proposed to improve the learning efficiency of the tracking model, and fully explore the spectral-spatial information. Specifically, an Adaptive Content and Position Embedding Module (ACPEM) is designed to dynamically learn the balance between focusing on positional and content-based information, which allows the model to effectively handle datasets of various sizes. To enhance the spectral-spatial information, a Spectral Grouping Module (SGM) is designed to learn the highfrequency information in complex scenarios, thereby enhancing diversified features. It operates in parallel with the ACPEM feature learning module. Furthermore, a Dynamic Reliability Refinement Module (DRRM) is incorporated to address challenges related to accurate object position perception, iteratively refining prediction parameters to enhance the reliability of the model. Extensive experiments demonstrate that the proposed HTACPE achieves satisfactory tracking performance both qualitatively and quantitatively, especially with insufficient training data.

Semantic segmentation of high-spatial-resolution (HSR) remote sensing (RS) images has been extensively studied, and most of the existing methods are based on convolutional neural network (CNN) models. However, the CNN is regarded to have less power in global representation modeling. In the past few years, methods using transformer have attracted increasing attention and generate improved results in semantic segmentation of natural images, owing to their powerful ability in global information acquisition. Nevertheless, these transformer-based methods exhibit limited performance in semantic segmentation of RS images, probably because of the lack of comprehensive understanding in the feature decoding process. In this paper, a novel transformer-based model named the bi-decoder transformer segmentor for remote sensing (BiTSRS) is proposed, aiming at alleviating the problem of flexible feature decoding, through a bi-decoder design for semantic segmentation of RS images. In the proposed BiTSRS, the Swin transformer is adopted as encoder to take both global and local representations into consideration, and a unique design module (ITM) is designed to deal with the limitation of input size for Swin transformer. Furthermore, BiTSRS adopts a bi-decoder structure consisting of a Dilated-Uper decoder and a fully deformable convolutional network (FDCN) module embedded with focal loss, with which it is capable of decoding a wide range of features and local detail deformations. Both ablation experiments and comparison experiments were conducted on three representative RS images datasets. The ablation analysis demonstrates the contributions of specifically designed modules in the proposed BiTSRS to performance improvement. The comparison experimental results illustrate that the proposed BiTSRS clearly outperforms some state-of-the-art semantic segmentation methods.

Given the ever-growing availability of remote sensing data (e.g., Gaofen in China, Sentinel in the EU, and Landsat in the USA), multimodal remote sensing techniques have been garnering increasing attention and have made extraordinary progress in various Earth observation (EO)-related tasks. The data acquired by different platforms can provide diverse and complementary information. The joint exploitation of multimodal remote sensing has been proven effective in improving the existing methods of land-use/land-cover segmentation in urban environments. To boost technical breakthroughs and accelerate the development of EO applications across cities and regions, one important task is to build novel cross-city semantic segmentation models based on modern artificial intelligence technologies and emerging multimodal remote sensing data. This leads to the development of better semantic segmentation models with high transferability among different cities and regions. The Cross-City Semantic Segmentation contest is organized in conjunction with the 13th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Hyperspectral imaging technology is of crucial importance to improve the performance of object tracking in many remote sensing surveillance areas. Previous methods primarily focused on feature fusion strategies by employing additional enhancement modules. However, these methods commonly lack contextual understanding to distinguish the target from the background and totally ignore the category information of the targets. To address these limitations, a novel hyperspectral object Tracker is proposed to incorporate Context-aware learning and Category consistency Tracker (CCTrack), which can adaptively learn context-aware representations in hyperspectral scenarios to obtain global target information with memory storage, while constructing an inter-frame category consistency constraint to enhance tracking process. Specifically, CCTrack integrates an Adaptive Context-aware Learning (ACL) mechanism, which includes a Feature Decoupling Module (FDM) to extract specific representations from decoupled features, and a Mamba layer to retain and update long-range dependencies. To align with prior knowledge of target recognition and motion patterns, an Alignment Transformation Module (ATM) is employed with ACL mechanism, fully leveraging spatial-spectral representations. Addtionally, a Category Consistency Constraint Modules (C3M) is introduced to enforce category consistency across frames by computing the similarities between the target features and the corresponding category name, serving as the constraint to improve tracking performance. Extensive experiments over the hyperspectral object tracking benchmark covering various remote sensing scenarios demonstrate that CCTrack outperforms state-of-the-art methods by a significant margin.