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Detection of drug residues in bean sprouts by hyperspectral imaging combined with 1DCNN with channel attention mechanism
Q Yang, L Yin, X Hu, L Wang - Microchemical Journal, 2024 - Elsevier
As a crucial vegetable food in Asia, bean sprouts are widely cultivated. However, the
common practice of using plant growth hormones and other drugs to boost yield and prolong …
common practice of using plant growth hormones and other drugs to boost yield and prolong …
Adaptive selection of spectral–spatial features for hyperspectral image classification using a modified-CBAM-based network
H Fu, C Wang, Z Chen - Neurocomputing, 2025 - Elsevier
Convolutional neural networks (CNNs) have demonstrated strong capabilities in
hyperspectral image (HSI) classification. However, it is still a challenge to adaptively adjust …
hyperspectral image (HSI) classification. However, it is still a challenge to adaptively adjust …
SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification
Polarimetric synthetic aperture radar (PolSAR) images encompass valuable information that
can facilitate extensive land cover interpretation and generate diverse output products …
can facilitate extensive land cover interpretation and generate diverse output products …
[HTML][HTML] TUH-NAS: A Triple-Unit NAS Network for Hyperspectral Image Classification
F Chen, B Su, Z Jia - Sensors (Basel, Switzerland), 2024 - pmc.ncbi.nlm.nih.gov
Over the last few years, neural architecture search (NAS) technology has achieved good
results in hyperspectral image classification. Nevertheless, existing NAS-based …
results in hyperspectral image classification. Nevertheless, existing NAS-based …
Lightweight dual-domain token learning method for hyperspectral image classification
Convolution neural network (CNN) and transformer-based methods have achieved
significant results in hyperspectral image (HSI) classification. These works tend to improve …
significant results in hyperspectral image (HSI) classification. These works tend to improve …
[PDF][PDF] 基于双分支残差网络的高光谱图像分类
杜天娇， 张永生， 包利东 - Laser & Optoelectronics Progress, 2024 - researching.cn
摘要高光谱图像分类是高光谱图像理解及应用的基础操作, 其准确率是衡量算法性能的关键指标
. 提出了一种新的双分支结构的残差网络(DSSRN), 该网络能够提取高光谱图像的鲁棒特征 …
. 提出了一种新的双分支结构的残差网络(DSSRN), 该网络能够提取高光谱图像的鲁棒特征 …