Huaxiang Song
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View article: Symmetrical Learning and Transferring: Efficient Knowledge Distillation for Remote Sensing Image Classification
Symmetrical Learning and Transferring: Efficient Knowledge Distillation for Remote Sensing Image Classification Open
Knowledge distillation (KD) is crucial for remote sensing image (RSI) classification, particularly as the operating environment in remote sensing is often constrained by hardware limitations. However, prior research has not fully addressed…
View article: Efficient Object Detection in Remote Sensing Images Using Quantitative Augmentation and Competitive Learning
Efficient Object Detection in Remote Sensing Images Using Quantitative Augmentation and Competitive Learning Open
Object detection in remote sensing images (RSIs) is crucial in Earth observation. However, current approaches often overlook key characteristics of RSIs, resulting in models that fail to balance accuracy and computational efficiency. To th…
View article: Pure data correction enhancing remote sensing image classification with a lightweight ensemble model
Pure data correction enhancing remote sensing image classification with a lightweight ensemble model Open
View article: Intrinsic self-sensing cementitious composites with hybrid nanofillers exhibiting excellent piezoresistivity
Intrinsic self-sensing cementitious composites with hybrid nanofillers exhibiting excellent piezoresistivity Open
Intrinsic self-sensing cementitious composites are competitive candidates for structural health monitoring of smart civil infrastructure. Meanwhile, the hybrid nanofillers can endow cementitious composites with excellent piezoresistivity w…
View article: Variance Consistency Learning: Enhancing Cross-Modal Knowledge Distillation for Remote Sensing Image Classification
Variance Consistency Learning: Enhancing Cross-Modal Knowledge Distillation for Remote Sensing Image Classification Open
Vision Transformers (ViTs) have demonstrated exceptional accuracy in classifying remote sensing images (RSIs). However, existing knowledge distillation (KD) methods for transferring representations from a large ViT to a more compact Convol…
View article: Efficient knowledge distillation for hybrid models: A vision transformer‐convolutional neural network to convolutional neural network approach for classifying remote sensing images
Efficient knowledge distillation for hybrid models: A vision transformer‐convolutional neural network to convolutional neural network approach for classifying remote sensing images Open
In various fields, knowledge distillation (KD) techniques that combine vision transformers (ViTs) and convolutional neural networks (CNNs) as a hybrid teacher have shown remarkable results in classification. However, in the realm of remote…
View article: ERKT-Net: Implementing Efficient and Robust Knowledge Distillation for Remote Sensing Image Classification
ERKT-Net: Implementing Efficient and Robust Knowledge Distillation for Remote Sensing Image Classification Open
The classification of Remote Sensing Images (RSIs) poses a significant challenge due to the presence of clustered ground objects and noisy backgrounds. While many approaches rely on scaling models to enhance accuracy, the deployment of RSI…
View article: High-quality agricultural development in the central China: Empirical analysis based on the Dongting Lake area
High-quality agricultural development in the central China: Empirical analysis based on the Dongting Lake area Open
High-quality agricultural development constitutes the primary point for forthcoming agricultural and rural endeavors. This study aimed to analyze the characteristics and obstacle factors of the high-quality agricultural development in cent…
View article: A Leading but Simple Classification Method for Remote Sensing Images
A Leading but Simple Classification Method for Remote Sensing Images Open
Recently, researchers have proposed a lot of deep convolutional neural network (CNN) approaches with obvious flaws to tackle the difficult semantic classification (SC) task of remote sensing images (RSI). In this paper, the author proposes…
View article: FST-EfficientNetV2: Exceptional Image Classification for Remote Sensing
FST-EfficientNetV2: Exceptional Image Classification for Remote Sensing Open
Recently, the semantic classification (SC) algorithm for remote sensing images (RSI) has been greatly improved by deep learning (DL) techniques, e.g., deep convolutional neural networks (CNNs). However, too many methods employ complex proc…
View article: Simple is best: A single-CNN method for classifying remote sensing images
Simple is best: A single-CNN method for classifying remote sensing images Open
Recently, researchers have proposed a lot of methods to boost the performance of convolutional neural networks (CNNs) for classifying remote sensing images (RSI). However, the methods' performance improvements were insignificant, while ti…
View article: A More Efficient Approach for Remote Sensing Image Classification
A More Efficient Approach for Remote Sensing Image Classification Open
Over the past decade, the significant growth of the convolutional neural network (CNN) based on deep learning (DL) approaches has greatly improved the machine learning (ML) algorithm’s performance on the semantic scene classification (SSC)…