|
|
|
|
|
Jun Wu, Xinyi Sun, Lei Qu, Xilan Tian and Guangyu Yang
Recently, deep learning tools have made significant progress in hyperspectral image (HSI) classification. Most of existing methods implement a patch-based classification manner which may cause training test information leakage or waste labeled informatio...
ver más
|
|
|
|
|
|
|
|
|
Razieh Pourdarbani, Sajad Sabzi, Mohsen Dehghankar, Mohammad H. Rohban and Juan I. Arribas
The presence of bruises on fruits often indicates cell damage, which can lead to a decrease in the ability of the peel to keep oxygen away from the fruits, and as a result, oxygen breaks down cell walls and membranes damaging fruit content. When chemical...
ver más
|
|
|
|
|
|
|
|
|
Du Wang, Xue Li, Fei Ma, Li Yu, Wen Zhang, Jun Jiang, Liangxiao Zhang and Peiwu Li
In this study, a fast and non-destructive method was proposed to analyze rapeseed quality parameters with the help of NIR hyperspectral imaging spectroscopy and chemometrics. Hyperspectral images were acquired in the reflectance mode. Meanwhile, the regi...
ver más
|
|
|
|
|
|
|
|
|
Guochao Zhang, Weijia Cao and Yantao Wei
With the development of the hyperspectral imaging technique, hyperspectral image (HSI) classification is receiving more and more attention. However, due to high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixe...
ver más
|
|
|
|
|
|
|
|
|
Jihyoung Ryu and Yeongmin Jang
Convolution neural networks have received much interest recently in the categorization of hyperspectral images (HSI). Deep learning requires a large number of labeled samples in order to optimize numerous parameters due to the expansion of architecture d...
ver más
|
|
|
|