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GSCAT-UNET: Enhanced U-Net model with spatial-channel attention gate and three-level attention for oil spill detection using SAR data.
Mar Pollut Bull
January 2025
JK Laxmipat University, Jaipur, Rajasthan, India.
Marine pollution due to oil spills presents major risks to coastal areas and aquatic life, leading to serious environmental health concerns. Oil Spill detection using SAR data has transitioned from traditional segmentation to a variety of machine learning & deep learning models like UNET proving its efficiency for the task. This research paper proposes a GSCAT-UNET model for efficient oil spill detection and discrimination from lookalikes.
View Article and Find Full Text PDFClassification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion.
Sensors (Basel)
January 2025
School of Oceanography and Spatial Information, China University of Petroleum East China-Qingdao Campus, Qingdao 266580, China.
Salt marsh vegetation in the Yellow River Delta, including (), (), and (), is essential for the stability of wetland ecosystems. In recent years, salt marsh vegetation has experienced severe degradation, which is primarily due to invasive species and human activities. Therefore, the accurate monitoring of the spatial distribution of these vegetation types is critical for the ecological protection and restoration of the Yellow River Delta.
View Article and Find Full Text PDFA Large-Scale Agricultural Land Classification Method Based on Synergistic Integration of Time Series Red-Edge Vegetation Index and Phenological Features.
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National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China.
Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China's agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE) and plant senescence reflectance index (PSRI).
View Article and Find Full Text PDFSuperparamagnetic Iron Oxide Nanoparticles as a Tracer for Sentinel Lymph Node Mapping in Endometrial Cancer.
Int J Mol Sci
January 2025
Department of Oncology, Wroclaw Medical University, 50-367 Wroclaw, Poland.
Sentinel lymph node (SLN) detection has been widely investigated in recent years as a part of the surgical staging of women with endometrial cancer (EC), gradually overtaking lymphadenectomy (LND) in this respect. In this study, thirty EC patients, assumed as stage I, were investigated using superparamagnetic iron oxide (SPIO) as a tracer for SLN detection followed by LND. The endpoints of this research were the proportion of successful SLN detection, the average number of SLNs per patient, the percentage of bilaterally detected SLNs, and the proportion of metastatic SLNs.
View Article and Find Full Text PDFApplication of the OSNA Technique (One-Step Nucleic Acid Amplification Test) in Breast Cancer.
Int J Mol Sci
January 2025
Laboratory of Cancer Genetics, Department of Pathology, Polish Mother's Memorial Hospital Research Institute, Rzgowska 281/289, 93-338 Lodz, Poland.
Breast cancer is one of the most common cancers diagnosed in both countries with high and low levels of socio-academic development. Routine, regular screening tests being introduced in an increasing number of countries make it possible to detect breast cancer at an early stage of development, as a result of which the trend in the incidence of metastatic breast cancer has been decreasing in recent years. The latest guidelines for the treatment of this tumor do not recommend axillary dissection, which limits the need for rapid assessment of the nodes during surgery.
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