Hyperspectral imaging (HSI) for intraoperative tumor cell classification [PDF]
R Beck +5 more
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Visible hyperspectral image (V-HSI) and thermal infrared hyperspectral image (TI-HSI) have been crucial data sources for land cover classification. V-HSI can directly provide information of land surface, such as shape, color, texture, and other features.
Enyu Zhao +5 more
doaj +1 more source
Joint Alternate Small Convolution and Feature Reuse for Hyperspectral Image Classification
A hyperspectral image (HSI) contains fine and rich spectral information and spatial information of ground objects, which has great potential in applications.
Hongmin Gao +4 more
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Structure Extraction With Total Variation for Hyperspectral Image Classification
This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV).
Qiaoqiao Li +4 more
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Improving Hyperspectral Image Classification with Compact Multi-Branch Deep Learning
The progress in hyperspectral image (HSI) classification owes much to the integration of various deep learning techniques. However, the inherent 3D cube structure of HSIs presents a unique challenge, necessitating an innovative approach for the efficient
Md. Rashedul Islam +3 more
doaj +1 more source
Since convolutional neural networks (CNN) can extract deeper features from hyperspectral images, they show good classification performance in the hyperspectral image (HSI) classification task. However, the performance of many CNN models is constrained by
Hongwei Wei +4 more
doaj +1 more source
An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification
Hyperspectral image (HSI) classification is a very important topic in remote sensing. There are many published methods for HSI classification in the literature. Nevertheless, it is not clear which method is the most robust to noise in HSI data cubes. In
Guangyi Chen, Adam Krzyzak, Shen-en Qian
doaj +1 more source
AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification
Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively ...
Xiaofei Yang +5 more
doaj +1 more source
Differentiating cytology of pancreatic ductal adenocarcinoma and pancreatic neuroendocrine tumors by EUS-FNA through hyperspectral imaging technology combined with artificial intelligence. [PDF]
Qin X +15 more
europepmc +1 more source
Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning. [PDF]
Neuwirthová E +9 more
europepmc +1 more source

