Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface

Remote Sensing, Год журнала: 2024, Номер 16(4), С. 665 - 665

Опубликована: Фев. 13, 2024

The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. multi-sensor enhances the accuracy information extraction. This research presents a comparison two classifiers (random forest extreme gradient boost (XGBoost)) using an features simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) Landsat 8 (optical) were used with textures enhanced modified indices to extract for year 2023. classification process utilized algorithms, random XGBoost, impervious surface study focused on three significant East Asian cities diverse dynamics: Jakarta, Manila, Seoul. proposed novel index called Normalized Blue Water Index (NBWI), which distinguishes water from other was as feature. Results showed overall 81% UIS XGBoost 77% RF while classifying land use cover into four major classes (water, vegetation, bare soil, impervious). However, framework classifier outperformed algorithm Dynamic World (DW) data product comparatively higher accuracy. Still, all show poor separability soil class compared ground truth data. accuracy, highlighting its potential

Язык: Английский

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Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics

Sensors, Год журнала: 2023, Номер 23(23), С. 9502 - 9502

Опубликована: Ноя. 29, 2023

The realm of medical imaging is a critical frontier in precision diagnostics, where the clarity image paramount. Despite advancements technology, noise remains pervasive challenge that can obscure crucial details and impede accurate diagnoses. Addressing this, we introduce novel teacher–student network model leverages potency our bespoke NoiseContextNet Block to discern mitigate with unprecedented precision. This innovation coupled an iterative pruning technique aimed at refining for heightened computational efficiency without compromising fidelity denoising. We substantiate superiority effectiveness approach through comprehensive suite experiments, showcasing significant qualitative enhancements across multitude modalities. visual results from vast array tests firmly establish method’s dominance producing clearer, more reliable images diagnostic purposes, thereby setting new benchmark

Язык: Английский

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A review on infrared and visible image fusion algorithms based on neural networks

Journal of Visual Communication and Image Representation, Год журнала: 2024, Номер 101, С. 104179 - 104179

Опубликована: Май 1, 2024

Infrared and visible image fusion represents a significant segment within the domain. The recent surge in processing hardware advancements, including GPUs, TPUs, cloud computing platforms, has facilitated of extensive datasets from multiple sensors. Given remarkable proficiency neural networks feature extraction fusion, their application infrared emerged as prominent research area years. This article begins by providing an overview current mainstream algorithms for based on networks, detailing principles various algorithms, representative works, respective advantages disadvantages. Subsequently, it introduces domain-relevant datasets, evaluation metrics, some typical scenarios. Finally, conducts qualitative quantitative evaluations results state-of-the-art offers future prospects experimental results.

Язык: Английский

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An image processing algorithm for image fusion via variational mode decomposition and its implementation on embedded platforms

Journal of Optics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

Язык: Английский

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High-precision multi-scale data fusion method for micro-nano CMM and white light interferometer

Optics & Laser Technology, Год журнала: 2025, Номер 188, С. 112927 - 112927

Опубликована: Апрель 8, 2025

Язык: Английский

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Detecting rice straw burning based on infrared and visible information fusion with UAV remote sensing

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 222, С. 109078 - 109078

Опубликована: Май 29, 2024

Язык: Английский

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Intelligent mask image reconstruction for cardiac image segmentation through local–global fusion

Applied Intelligence, Год журнала: 2025, Номер 55(4)

Опубликована: Янв. 3, 2025

Язык: Английский

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Bimodal and trimodal image fusion: A study of subjective scores and objective measures

Journal of Electrical Engineering, Год журнала: 2025, Номер 76(1), С. 7 - 17

Опубликована: Фев. 1, 2025

Abstract Thermal vision significantly enhances visibility under various environmental conditions. So, this paper presents a comprehensive study on the importance of thermal in improving image fusion human visual perception through subjective evaluation. The focuses three imaging sensors commonly used computer applications: long-wavelength infrared (LWIR), visible (VIS), and near-infrared (NIR). Four alternatives (LWIR+VIS, LWIR+NIR, NIR+VIS, LWIR+NIR+VIS) are produced using reliable deep learning approach assessed both tests objective metrics. evaluation is performed involving 15 military students officers from University Defence Belgrade, while assessment elaborated eight no-reference measures. Results indicate that fused images with information show better performance than non-thermal based alternative (NIR+VIS). Moreover, LWIR+NIR+VIS LWIR+NIR provide similar appearance, demonstrating bimodal (LWIR+NIR) can be sufficient to produce highly informative image. Additionally, degree agreement between scores calculated. simple edge intensity measure shows highest agreement, entropy demonstrates second-best score.

Язык: Английский

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Natural statistics of multisensor images: Comparative analysis and application to image classification and image fusion

Infrared Physics & Technology, Год журнала: 2025, Номер unknown, С. 105780 - 105780

Опубликована: Фев. 1, 2025

Язык: Английский

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Focus-aware and deep restoration network with transformer for multi-focus image fusion

Digital Signal Processing, Год журнала: 2024, Номер 149, С. 104473 - 104473

Опубликована: Март 19, 2024

Язык: Английский

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