While transformers are powerful neural network architectures for feature learning, current Transformer-based approaches for semantic segmentation of high-resolution remote sensing images (HRRSIs) struggle with the extraction of local semantic features. To address this issue, we incorporate a hypergraph into the Transformer. Hypergraph-based methods are proficient at discovering high-order correlations within limited-scale data, extracting pertinent representations to enhance the Transformer’s learning capabilities. We also propose dual pooling and feature aggregation modules, inspired by the adaptive pooling’s potent local modeling capabilities, to additionally extract fine-grained features from HRRSIs. In particular, we conceive a Hypergraph Biformer (HGBT) based on these three proposed modules along with a Biformer backbone. HGBT has the potential to learn general latent features as well as generate high-order representations of HRRSIs by modeling correlations of multi-scale features and local topology within an entirely nonlinear space, leading to the aggregation of features in a compact and localized manner, enhancing the model’s ability to capture detailed variations within small areas. We validate our approach through extensive experiments on ISPRS Vaihingen and Potsdam datasets, where HGBT attains mIoU of 83.71% and 87.88%, respectively. Both quantitative and qualitative assessments underscore the dominance of HGBT. Our code will be accessible at https://github.com/ZhangIceNight/HGBFormer.

Forest remote sensing change detection provides an important technical support for forest management decisions and analysis of forest disturbance factors. However, lack of data in specialized fields leads to the detection accuracy improved difficulty. In this study, a forest remote sensing change detection model in the context of few-shot learning is proposed. The proposed model achieves end-to-end change detection algorithm for forest scenes from two perspectives of data augmentation and updated few-shot algorithm. Firstly, forest fragments images are jointly generated by using feature extraction network and generative adversarial network. Then, forest fragments are blended into the original change detection dataset by Poisson blending method to achieve effective augmentation. Furthermore, the end-to-end change detection network is also updated using a few-shot learning. Additionally, Meta-learning module is added to the slow feature analysis algorithm based on the multi-attention mechanism to realize the detection effect improvements. The proposed model improves the F1 score from 86% to 91% on the two datasets. Moreover, it increases the F1 score by 6.52% on average.

The accuracy of current deep learning algorithms has certainly increased. However, deploying deep learning networks on edge devices with limited resources is challenging due to their inherent depth and high parameter count. Here, we proposed an improved YOLO model based on an attention mechanism and receptive field (RFA-YOLO) model, applying the MobileNeXt network as the backbone to reduce parameters and complexity, adopting the Receptive Field Block (RFB) and Efficient Channel Attention (ECA) modules to improve the detection accuracy of multi-scale and small objects. Meanwhile, an FPGA-based model deployment solution was proposed to implement parallel acceleration and low-power deployment of the detection algorithm model, which achieved real-time object detection for optical remote sensing images. We implement the proposed DPU and Vitis AI-based object detection algorithms with FPGA deployment to achieve low power consumption and real-time performance requirements. Experimental results on DIOR dataset demonstrate the effectiveness and superiority of our RFA-YOLO model for object detection algorithms. Moreover, to evaluate the performance of the proposed hardware implementation, it was implemented on a Xilinx ZCU104 board. Results of the experiments for hardware and software simulation show that our DPU-based hardware implementation are more power efficient than central processing units (CPUs) and graphics processing units (GPUs), and have the potential to be applied to onboard processing systems with limited resources and power consumption.

Effective forest tree species classification is critical for various application domains such as forest management, bio-diversity conservation, and ecological research. However, existing studies on tree species classification predominantly rely on high-cost and processing-intensive hyperspectral data, which limits practical applications on large scales. In this work, we focus on investigating the potential of cost-effective UAV RGB images for tree species classification in heterogeneous forests and propose a method that fully leverages the rich spatial, semantic, and visible spectral information of UAV RGB images. We propose an RSVMamba model, which incorporates improved Visual State Space Blocks and an AutoDownsampling module to enhance accuracy and stability, while paying particular attention to small objects in sparse spatial locations. The model achieves linear computational complexity while retaining the global receptive field, making it particularly suitable for processing high spatial-resolution images. Additionally, we collected UAV RGB images covering 40 km2 of subtropical forest in southern China. A meticulous evaluation on this data shows that our method achieves an overall accuracy of 84.28% for eight tree species, dead trees, and other broadleaves. We verify the superiority of our method through a series of comparative experiments on the collected and benchmark datasets. Our results affirm the usefulness of single-temporal UAV RGB images for tree species classification in heterogeneous forest environments. Furthermore, the proposed method bridges the gap between data accessibility and precision in tree species classification, broadening the boundaries of single-temporal UAV RGB images for practical forestry applications and providing a more cost-effective and time-flexible solution for this problem.