Luojia-HSSR: A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

Xu, Yue; Gong, Jianya; Huang, Xin; Hu, Xiangyun; Li, Qiang; Peng, Min

doi:10.1080/10095020.2022.2070555

Cited by 30 publications

(2 citation statements)

References 32 publications

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“…This study discussed the preprocessing, features engineering, and classification techniques for the land cover classification. few more studies are also introduced, such as the highresolution adaptive model [30], 3D-HRNET [31], crop classification [32], and the Northern Border Region technique [33].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

Albarakati,

Khan,

Hamza

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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AI-driven precision agriculture applications can benefit from the large data source that remote sensing provides, as it can gather agricultural monitoring data at various scales throughout the year. Numerous advantages for sustainable agricultural applications, including yield prediction, crop monitoring, and climate change adaptation, can be obtained from remote sensing and artificial intelligence. In this work, we proposed a fully automated Optimized Self-Attention Fused Convolutional Neural Network (CNN) architecture for land use and land cover classification using remote sensing (RS) data. A new contrast enhancement equation has been proposed and utilized in the proposed architecture for the data augmentation. After that, a fused Self-Attention CNN architecture was proposed. The proposed architecture initially consists of two custom models named IBNR-65 and Densenet-64. Both models have been designed based on the inverted bottleneck residual mechanism and Dense Blocks. After that, both models were fused using a depth-wise concatenation and append a Self-Attention layer for deep features extraction. After that, we trained the model and performed classification using neural network (NN) classifiers. The results obtained from the NN classifiers are insufficient; therefore, we implemented a Bayesian Optimization and fine-tuned the hyperparameters of NN. In addition, we proposed a Quantum Hippopotamus Optimization Algorithm for the best feature selection. The selected features are finally classified using fine-tuned NN classifiers and obtained improved accuracy of 98.20, 89.50, and 91.70%, and the highest precision rate is 98.23, recall is 98.20, and f1-score is 98.21 respectively, for SIRI-WHU, EuroSAT, and NWPU datasets. Moreover, a detailed ablation study was conducted, and the performance was compared with SOTA. The proposed architecture shows

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Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

Albarakati,

Khan,

Hamza

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Remote sensing images serve as a crucial data source, offering rich texture details essential for various fields, like change detection [1], target recognition [2], and land cover classification [3]. Their significance in the transportation industry cannot be overstated since these images provide indispensable insights for planning, design, construction, and monitoring of infrastructure [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization

Bian,

Liu,

Chen

2024

Applied Sciences

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In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network’s performance while neglecting the equally crucial aspect of inference speed. In this study, we propose a method for lightweight super-resolution reconstruction of remote sensing images, termed SRRepViT. This approach reduces model parameters and floating-point operations during inference through parameter equivalent transformation. Using the RSSOD remote sensing dataset as our benchmark dataset, we compared the reconstruction performance, inference time, and model size of SRRepViT with other classical methods. Compared to the lightweight model ECBSR, SRRepViT exhibits slightly improved reconstruction performance while reducing inference time by 16% and model parameters by 34%, respectively. Moreover, compared to other classical super-resolution reconstruction methods, the SRRepViT model achieves similar reconstruction performance while reducing model parameters by 98% and increasing inference speed by 90% for a single remote sensing image.

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A residual semantic graph convolutional network with high-resolution representation for 3D human pose estimation in a virtual fashion show

Zhang,

Ding,

et al. 2024

Multimed Tools Appl

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Luojia-HSSR: A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

Cited by 30 publications

References 32 publications

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization

A residual semantic graph convolutional network with high-resolution representation for 3D human pose estimation in a virtual fashion show

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