Change detection from high-resolution remote sensing images lies as a cornerstone of Earth observation applications, yet its efficacy is often compromised by two critical challenges. First, false alarms are prevalent as models misinterpret radiometric variations from temporal shifts (e.g., illumination, season) as genuine changes. Second, a non-negligible semantic gap between deep abstract features and shallow detail-rich features tends to obstruct their effective fusion, culminating in poorly delineated boundaries. To step further in addressing these issues, we propose the Frequency-Spatial Synergistic Gated Network (FSG-Net), a novel paradigm that aims to systematically disentangle semantic changes from nuisance variations. Specifically, FSG-Net first operates in the frequency domain, where a Discrepancy-Aware Wavelet Interaction Module (DAWIM) adaptively mitigates pseudo-changes by discerningly processing different frequency components. Subsequently, the refined features are enhanced in the spatial domain by a Synergistic Temporal-Spatial Attention Module (STSAM), which amplifies the saliency of genuine change regions. To finally bridge the semantic gap, a Lightweight Gated Fusion Unit (LGFU) leverages high-level semantics to selectively gate and integrate crucial details from shallow layers. Comprehensive experiments on the CDD, GZ-CD, and LEVIR-CD benchmarks validate the superiority of FSG-Net, establishing a new state-of-the-art with F1-scores of 94.16%, 89.51%, and 91.27%, respectively. The code will be made available at https://github.com/zxXie-Air/FSG-Net after a possible publication.
This study examines the selective depression of calcite and dolomite during phosphate flotation using biobased depressants; carboxymethyl cellulose (CMC) and xanthan gum (XG); with a fatty acid collector. Single mineral flotation, Fourier transform infrared spectroscopy (FTIR), adsorption isotherms, contact angle, surface tension, and atomic force microscopy (AFM) analyses were combined to elucidate their adsorption mechanisms. At 45 mg/L and pH 10, XG and CMC reduced calcite recovery from 92 +/- 0.87 % to <5 +/- 0.44 % and dolomite from 88 +/- 0.94 % to 3 +/- 0.24 %, while fluorapatite recovery remained 85-87 +/- 0.4 %, demonstrating strong selectivity toward carbonates. FTIR showed diminished collector carboxylate bands on carbonates after depressant pretreatment, while signals persisted on fluorapatite. Contact angle measurements confirmed suppressed hydrophobization of carbonates (24.5 degrees for XG + FAC; 27.7 degrees for CMC + FAC). AFM revealed polymer film formation on carbonate surfaces, and surface tension results suggested limited polymer-collector complexation at the air-water interface. Overall, selective depression originates from competitive adsorption and site blocking at the solid-liquid interface, influenced by pH and surface chemistry.
Change captioning has become essential for accurately describing changes in multitemporal remote sensing data, providing an intuitive way to monitor Earth's dynamics through natural language. However, existing change captioning methods face two key challenges. First, the use of multistage feature fusion strategies aims to achieve better change detection results by combining information from multiple stages. This approach leads to high computational demands, as it requires intensive processing across different feature levels. Second, previous methods mainly focus on feature extraction and fusion along the image spatial dimensions, overlooking the importance of channel-wise information. This leads to insufficient semantic extraction, resulting in inadequate detail in object descriptions and limiting the ability to fully capture the changes. To solve these challenges, we propose a spatial-channel attention encoder (SCE) based on the transformer model for remote sensing change captioning, which is named SCNet. In particular, SCNet integrates an SCE, a difference-guided fusion module, and a caption decoder. Compared to typical models that require multistage fusion in the transformer encoder and fusion module. By jointly modeling spatial and channel information in SCE, our approach significantly enhances the model's ability to extract semantic information from objects in multitemporal remote sensing images. Extensive experiments validate the effectiveness of SCNet, achieving CIDEr scores of 140.23% on the LEVIR-CC dataset and 97.74% on the DUBAI-CC dataset, surpassing current state-of-the-art (SOTA) methods.
The Eastern Erzgebirge (Germany) and Krusne hory (Czech Republic / Czechia) region hosts prolific Li-(Sn-W) deposits, all linked to late-stage magma evolution and magmatic-hydrothermal alteration within a Caldera collapse system. Whereas the geochemical controls are relatively well understood, tectonic controls on magma emplacement are not. Here, we aim to explain the tectonic controls on trans-crustal caldera-forming magmatic systems, and link these to more local controls on fertile magmatism. This is achieved by compiling and reviewing available geological, geochronological, geophysical, and structural data, and integrating them to derive a framework for late-to post-Variscan tectonics and magmatism. Specifically, we link the main faults in the vicinity of the Altenberg-Teplice and Tharandt calderas with the western middle Pennsylvanian (similar to 314-312 Ma) Bohemian basin system, to propose a major transtensional linkage structure between the Elbe Shear Zone and Pfahl or Danube Shear Zones. We propose that these transtensional pull-apart basins and dextral strike-slip fault systems do not only localize crustal-scale magmatic systems and associated calderas, but also exert a more local control on intra-caldera intrusive stocks that are host to greisen-type Li-(Sn-W) ore deposits in the Eastern Erzgebirge / Krusne hory region.
The use of autonomous unmanned aerial vehicle (UAV) swarms for area coverage requires efficient coverage path planning (CPP) strategies that ensure complete exploration while minimizing maneuvering effort, energy consumption, and collision risk. This paper proposes a distributed computational framework for swarm-based patrolling using CPP algorithms. The framework integrates B & eacute;zier-curve trajectory smoothing and safety-distance constraints to generate dynamically feasible and collision-free paths. A capability-aware space decomposition method partitions the target region into convex subareas, enabling parallel coverage while accounting for UAV configuration and platform capabilities. Swarm-adapted versions of Parallel, Square, LMAT, and SCAN strategies are developed to generate intra-and inter-subregion coverage paths. Experimental validation using a homogeneous swarm of four quadcopters demonstrates reduced computational complexity and turning maneuvers while producing smooth and continuous trajectories, enabling efficient large-area coverage with improved operational endurance.
