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Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery.
Sci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
View Article and Find Full Text PDFForest pest monitoring and early warning using UAV remote sensing and computer vision techniques.
Sci Rep
January 2025
College of Computer and Control Engineering, Northeast Forestry University, Haerbin, 150040, Heilongjiang, China.
Unmanned aerial vehicle (UAV) remote sensing has revolutionized forest pest monitoring and early warning systems. However, the susceptibility of UAV-based object detection models to adversarial attacks raises concerns about their reliability and robustness in real-world deployments. To address this challenge, we propose SC-RTDETR, a novel framework for secure and robust object detection in forest pest monitoring using UAV imagery.
View Article and Find Full Text PDFAssessment of body composition and prediction of infectious pancreatic necrosis via non-contrast CT radiomics and deep learning.
Front Microbiol
December 2024
Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
Aim: The current study aims to delineate subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), the sacrospinalis muscle, and all abdominal musculature at the L3-L5 vertebral level from non-contrast computed tomography (CT) imagery using deep learning algorithms. Subsequently, radiomic features are collected from these segmented images and subjected to medical interpretation.
Materials And Methods: This retrospective analysis includes a cohort of 315 patients diagnosed with acute necrotizing pancreatitis (ANP) who had undergone comprehensive whole-abdomen CT scans.
An AttSDNet model for multi-scale feature perception enhanced remote sensing classification of coastal salt-marsh wetlands.
Mar Environ Res
December 2024
Hydro-environmental Research Centre, School of Engineering, Cardiff University, CF10 3AT, Cardiff, UK.
Coastal salt-marsh wetlands have important ecological value, and play an important role in coastal blue carbon sink. However, under the influence of various external and natural factors, coastal wetland ecosystems worldwide have severely degraded, leading to biodiversity loss and ecological damage. Based on satellite remote sensing data and deep learning methods, it is an effective means to quickly monitor the spatial distribution of coastal wetlands, which is very important for the protection and restoration of coastal wetlands.
View Article and Find Full Text PDFDetecting and localizing cervical lesions in colposcopic images with deep semantic feature mining.
Front Oncol
November 2024
Hepatopancreatobiliary Surgery Department, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Objective: This study aims to investigate the feasibility of employing artificial intelligence models for the detection and localization of cervical lesions by leveraging deep semantic features extracted from colposcopic images.
Methods: The study employed a segmentation-based deep learning architecture, utilizing a deep decoding network to integrate prior features and establish a semantic segmentation model capable of distinguishing normal and pathological changes. A two-stage decision model is proposed for deep semantic feature mining, which combines image segmentation and classification to categorize pathological changes present in the dataset.
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