The semantic segmentation of satellite and UAV remote sensing imagery is pivotal for address exploration, change detection, quantitative analysis and urban planning. Recent advancements have seen an influx of segmentation networks utilizing convolutional neural networks and transformers. However, the intricate geographical features and varied land cover boundary interferences in remote sensing imagery still challenge conventional segmentation networks’ spatial representation and long-range dependency capabilities. This paper introduces a novel U-Net-like network for UAV image segmentation. We developed a link aggregation Mamba at the critical skip connection stage of UNetFormer. This approach maps and aggregates multi-scale features from different stages into a unified linear dimension through four Mamba branches containing state-space models (SSMs), ultimately decoupling and fusing these features to restore the contextual relationships in the mask. Moreover, the Mix-Mamba module is incorporated, leveraging a parallel self-attention mechanism with SSMs to merge the advantages of a global receptive field and reduce modeling complexity. This module facilitates nonlinear modeling across different channels and spaces through multipath activation, catering to international and local long-range dependencies. Evaluations on public remote sensing datasets like LovaDA, UAVid and Vaihingen underscore the state-of-the-art performance of our approach.

With the objective of addressing the problem of the fixed convolutional kernel of a standard convolution neural network and the isotropy of features making 3D point cloud data ineffective in feature learning, this paper proposes a point cloud processing method based on graph convolution multilayer perceptron, named GC-MLP. Unlike traditional local aggregation operations, the algorithm generates an adaptive kernel through the dynamic learning features of points, so that it can dynamically adapt to the structure of the object, i.e., the algorithm first adaptively assigns different weights to adjacent points according to the different relationships between the different points captured. Furthermore, local information interaction is then performed with the convolutional layers through a weight-sharing multilayer perceptron. Experimental results show that, under different task benchmark datasets (including ModelNet40 dataset, ShapeNet Part dataset, S3DIS dataset), our proposed algorithm achieves state-of-the-art for both point cloud classification and segmentation tasks.

Leveraging thermal infrared imagery to complement RGB spatial information is a key technology in industrial sensing. This technology enables mobile devices to perform scene understanding through RGB-T semantic segmentation. However, existing networks conduct only limited information interaction between modalities and lack specific designs to exploit the thermal aggregation entropy of the thermal modality, resulting in inefficient feature complementarity within bilateral structures. To address these challenges, we propose Wavelet-CNet for RGB-T semantic segmentation. Specifically, we design a Wavelet Cross Fusion Module (WCFM) that applies wavelet transforms to separately extract four types of low- and high-frequency information from RGB and thermal features, which are then fed back into attention mechanisms for dual-modal feature reconstruction. Furthermore, a Cross-Scale Detail Enhancement Module (CSDEM) introduces cross-scale contextual information from the TIR branch into each fusion stage, aligning global localization through contour information from thermal features. Wavelet-CNet achieves competitive mIoU scores of 58.3% and 85.77% on MFNet and PST900, respectively, while ablation studies on MFNet further validate the effectiveness of the proposed WCFM and CSDEM modules.

In real-world scenarios, due to factors like sensor noise, point cloud data often exhibits low overlap, posing challenges for traditional registration methods. To address this issue, we propose a low-overlap point cloud registration with Transformer. This algorithm employs a dynamic positional encoding strategy that adaptively computes position encodings for each point based on its distribution. This enables better capturing of richer spatial relationships between point clouds and facilitates adaptation to diverse point cloud distributions across various scenes. Furthermore, we combine the mechanisms of self-attention and graph convolutions. The self-attention mechanism captures global dependencies among points, while the graph convolutions capture local neighborhood information between points. Lastly, in the context of cross-attention, adaptive weights are introduced during the attention calculation process. This involves multiplying attention scores by adaptive weights, enhancing the model's ability to focus on crucial registration areas. In scenarios with low overlap, this algorithm significantly enhances the success rate of successful registrations. It achieves notable improvements and attains a new state-of-the-art performance in the 3DLoMatch benchmark test, reaching a registration recall rate of 71.7%.

Existing salient object detection methods for optical remote sensing images still face certain limitations due to complex background variations, significant scale discrepancies among targets, severe background interference, and diverse topological structures. On the one hand, the feature transmission process often neglects the constraints and complementary effects of high-level features on low-level features, leading to insufficient feature interaction and weakened model representation. On the other hand, decoder architectures generally rely on simple cascaded structures, which fail to adequately exploit and utilize contextual information. To address these challenges, this study proposes a Hierarchical Semantic Interaction Module to enhance salient object detection performance in optical remote sensing scenarios. The module introduces foreground content modeling and a hierarchical semantic interaction mechanism within a multi-scale feature space, reinforcing the synergy and complementarity among features at different levels. This effectively highlights multi-scale and multi-type salient regions in complex backgrounds. Extensive experiments on multiple optical remote sensing datasets demonstrate the effectiveness of the proposed method. Specifically, on the EORSSD dataset, our full model integrating both CA and PA modules improves the max F-measure from 0.8826 to 0.9100 (↑2.74%), increases maxE from 0.9603 to 0.9727 (↑1.24%), and enhances the S-measure from 0.9026 to 0.9295 (↑2.69%) compared with the baseline. These results clearly demonstrate the effectiveness of the proposed modules and verify the robustness and strong generalization capability of our method in complex remote sensing scenarios.