In recent years, weakly supervised semantic segmentation (WSSS) has garnered significant attention in remote sensing image analysis due to its low annotation cost. To address the issues of inaccurate and incomplete seed areas and unreliable pseudo masks in WSSS, we propose a novel WSSS method for remote sensing images based on the Siamese Affinity Network (SAN) and the Segment Anything Model (SAM). First, we design a seed enhancement module for semantic affinity, which strengthens contextual relevance in the feature map by enforcing a unified constraint principle of cross-pixel similarity, thereby capturing semantically similar regions within the image. Second, leveraging the prior notion of cross-view consistency, we employ a Siamese network to regularize the consistency of CAMs from different affine-transformed images, providing additional supervision for weakly supervised learning. Finally, we utilize the SAM segmentation model to generate semantic superpixels, expanding the original CAM seeds to more completely and accurately extract target edges, thereby improving the quality of segmentation pseudo masks. Experimental results on the large-scale remote sensing datasets DRLSD and ISPRS Vaihingen demonstrate that our method achieves segmentation performance close to that of fully supervised semantic segmentation (FSSS) methods on both datasets. Ablation studies further verify the positive optimization effect of each module on segmentation pseudo labels. Our approach exhibits superior localization accuracy and precise visualization effects across different backbone networks, achieving state-of-the-art localization performance.

Autonomous drone navigation relies on onboard sensors to perceive obstacle information in real time. However, indoor environments contain abundant wall structures that occlude the sensor’s field of view, rendering obstacle information within occluded regions undetectable to the drone. Existing trajectory planning algorithms fail to adequately account for the safety risks introduced by sensor occlusion. To address this limitation, this article proposes a novel trajectory planning framework to enhance drone flight performance in indoor environments. Specifically, a 3D occupancy grid map is first constructed from sensor data, and an initial trajectory is generated from the current position to the goal. A sensor occlusion detection algorithm then classifies the current scene into three categories: occlusion-free, partial occlusion, and full occlusion. For occlusion-free scenarios, the initial trajectory is directly forwarded to the controller. For partial and full occlusion cases, an occlusion-aware trajectory replanning algorithm generates multiple candidate trajectories in unknown regions. These candidates are evaluated by a scoring function comprising three metrics: safety, efficiency, and smoothness. Upon detection of a collision between the currently executing initial trajectory and an obstacle, the active trajectory is immediately switched to the highest-scoring candidate trajectory, thereby ensuring both flight safety and navigation efficiency of the drone. Extensive experiments are conducted across multiple occlusion scene configurations to validate the performance of the proposed method. Experimental results demonstrate that the proposed method is capable of providing safe and efficient trajectories for drones under both partial occlusion and full occlusion conditions.

Current methods for formation flight primarily focus on maintaining formations, often neglecting the swarm's agility. Furthermore, most of these approaches fail to leverage global information from the swarm for obstacle avoidance, making them incapable of generating efficient and safe trajectories in large obstacle scenarios. To address these limitations, this letter proposes a novel swarm trajectory planning framework that utilizes a virtual core to control the swarm. We employ virtual core penalties and dynamic maximum speed allocation to strike a balance between swarm flexibility and formation keeping, allowing the drones to avoid obstacles more smoothly and safely while maintaining formation stability. For large obstacle avoidance, we design a collaborative large obstacle boundary search strategy and a global swarm planning method to enable the rapid and safe generation of drone trajectories. To validate the performance of the proposed method, we develop a comprehensive set of experimental scenarios that include both simulations and real-world environments. The experimental results confirm the effectiveness of our approach.