[HTML][HTML] Current advances in imaging spectroscopy and its state-of-the-art applications
Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single
system and has gained widespread acceptance as a non-destructive scientific instrument for …
system and has gained widespread acceptance as a non-destructive scientific instrument for …
[HTML][HTML] Feature construction methods for processing and analysing spectral images and their applications in food quality inspection
Background Hyperspectral imaging (HSI) technology fusing spectroscopic technology and
imaging technology has been proposed to achieve rapid and non-destructive inspection of …
imaging technology has been proposed to achieve rapid and non-destructive inspection of …
Morphological transformation and spatial-logical aggregation for tree species classification using hyperspectral imagery
Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …
contribute to a more accurate identification of materials and land covers. However, most …
Dimensionality reduction and classification of hyperspectral image via multistructure unified discriminative embedding
Graph can achieve good performance to extract the low-dimensional features of
hyperspectral image (HSI). However, the present graph-based methods just consider the …
hyperspectral image (HSI). However, the present graph-based methods just consider the …
Feedback attention-based dense CNN for hyperspectral image classification
C Yu, R Han, M Song, C Liu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Hyperspectral image classification (HSIC) methods based on convolutional neural network
(CNN) continue to progress in recent years. However, high complexity, information …
(CNN) continue to progress in recent years. However, high complexity, information …
Central attention network for hyperspectral imagery classification
In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two
principles for spectral–spatial feature extraction of HSI are built, including the foundation of …
principles for spectral–spatial feature extraction of HSI are built, including the foundation of …
Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing
K Xu, H Huang, P Deng, Y Li - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
Scene classification of high spatial resolution (HSR) images can provide data support for
many practical applications, such as land planning and utilization, and it has been a crucial …
many practical applications, such as land planning and utilization, and it has been a crucial …
Three-dimensional singular spectrum analysis for precise land cover classification from UAV-borne hyperspectral benchmark datasets
The precise classification of land covers with hyperspectral imagery (HSI) is a major
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …
Semi-supervised multiscale dynamic graph convolution network for hyperspectral image classification
In recent years, convolutional neural networks (CNNs)-based methods achieve cracking
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …
A novel band selection and spatial noise reduction method for hyperspectral image classification
As an essential reprocessing method, dimensionality reduction (DR) can reduce the data
redundancy and improve the performance of hyperspectral image (HSI) classification. A …
redundancy and improve the performance of hyperspectral image (HSI) classification. A …