Graph-weighted contrastive learning for semi-supervised hyperspectral image classification

Journal of Electronic Imaging, Год журнала: 2025, Номер 34(02)

Опубликована: Апрель 11, 2025

Язык: Английский

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ResMamba: A state–space model approach and benchmark dataset for precise forage identification in desert rangelands

Expert Systems with Applications, Год журнала: 2025, Номер 280, С. 127411 - 127411

Опубликована: Апрель 11, 2025

Язык: Английский

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Cross-domain multimodal feature enhancement hypergraph neural network for few-shot hyperspectral images classification

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127742 - 127742

Опубликована: Апрель 1, 2025

Язык: Английский

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Integration of Hyperspectral Imaging and AI Techniques for Crop Type Mapping: Present Status, Trends, and Challenges

Remote Sensing, Год журнала: 2025, Номер 17(9), С. 1574 - 1574

Опубликована: Апрель 29, 2025

Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly effective in the precise discrimination types. This systematic review examines evolution platforms, from Unmanned Aerial Vehicle (UAV)-mounted sensors space-borne satellites (e.g., EnMAP, PRISMA), explores recent scientific advances AI methodologies mapping. A protocol was applied identify 47 studies databases peer-reviewed publications, focusing on sensors, input features, classification architectures. analysis highlights significant contributions Deep Learning (DL) models, particularly Vision Transformers (ViTs) hybrid architectures, improving accuracy. However, also identifies critical gaps, including under-utilization limited multi-sensor need modeling approaches such as Graph Neural Networks (GNNs)-based methods geospatial foundation (GFMs) large-scale type Furthermore, findings highlight importance developing scalable, interpretable, transparent maximize potential imaging (HSI), underrepresented regions Africa, where research remains limited. provides valuable insights guide future researchers adopting HSI reliable mapping, contributing sustainable agriculture global

Язык: Английский

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CenterMamba: Enhancing Semantic Representation with Center-Scan Mamba Network for Hyperspectral Image Classification

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127985 - 127985

Опубликована: Май 1, 2025

Язык: Английский

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Multimodal medical image fusion based on dilated convolution and attention-based graph convolutional network

Computers & Electrical Engineering, Год журнала: 2025, Номер 124, С. 110359 - 110359

Опубликована: Май 1, 2025

Язык: Английский

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Graph-Transformer with spatial-spectral features fusion for hyperspectral image classification

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125962 - 125962

Опубликована: Ноя. 1, 2024

Язык: Английский

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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (Annona squamosa L.)

Agronomy, Год журнала: 2024, Номер 15(1), С. 38 - 38

Опубликована: Дек. 27, 2024

One of the most important nutrients needed for fruit tree growth is nitrogen. For orchards to get targeted, well-informed nitrogen fertilizer, accurate, large-scale, real-time monitoring, and assessment nutrition essential. This study examines Leaf Nitrogen Content (LNC) custard apple tree, a noteworthy that extensively grown in China’s Yunnan Province. uses an ensemble learning technique based on multiple machine algorithms effectively precisely monitor leaf content canopy using multispectral footage trees taken via Unmanned Aerial Vehicle (UAV) across different phases. First, shadows background noise from soil are removed UAV imagery by spectral shadow indices The noise-filtered then used extract number vegetation (VIs) textural features (TFs). Correlation analysis determine which pertinent LNC estimation. A two-layer model built quantitatively estimate stacking (Stacking) principles. Random Forest (RF), Adaptive Boosting (ADA), Gradient Decision Trees (GBDT), Linear Regression (LR), Extremely Randomized (ERT) among basis estimators integrated first layer. By detecting eliminating redundancy base estimators, Least Absolute Shrinkage Selection Operator regression (Lasso)model second layer improves According results, Lasso successfully finds redundant suggested approach, yields maximum estimation accuracy trees’ leaves. With root mean square error (RMSE) 0.059 absolute (MAE) 0.193, coefficient determination (R2) came 0. 661. significant potential UAV-based techniques tracking leaves highlighted this work. Additionally, approaches investigated might offer insightful information point reference remote sensing applications monitoring other crops.

Язык: Английский

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Local-global feature fusion network for hyperspectral image classification

International Journal of Remote Sensing, Год журнала: 2024, Номер unknown, С. 1 - 28

Опубликована: Окт. 4, 2024

Язык: Английский

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