In recent years, there has been a growing emphasis on assessing and ensuring the quality
of horticultural and agricultural produce. Traditional methods involving field measurements …

Convolutional neural networks (CNNs) have been widely employed for hyperspectral image
(HSI) classification due to their powerful ability to extract local spatial features. However …

In recent years, significant breakthroughs have been achieved in hyperspectral image (HSI)
processing using deep learning techniques, including classification, object detection, and …

Hyperspectral imaging has a strong capability respecting distinguishing surface objects due
to the ability of collect hundreds of bands along the electromagnetic spectrum. Hyperspectral …

Federated learning is a promising solution in several industries for cotraining models among
distributed clients via centralized servers without leaving private user data on the devices …

In the rapidly evolving realm of remote sensing technology, the classification of
Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent …

Although convolutional neural networks (CNNs) have shown superior performance to
traditional machine learning algorithms for hyperspectral image (HSI) classification tasks …

Abstract Land Use/Land Cover (LULC) classification using hyperspectral images in remote
sensing is a leading technology. However, LULC classification using hyperspectral images …

Traditional convolutional neural networks (CNNs) can be applied to obtain the spectral-
spatial feature information from hyperspectral images (HSIs). However, they often introduce …

Recently, graph convolutional networks (GCNs) has attracted wide attention on the wetland
classification with limited samples. However, traditional approaches of superpixel …