Recent advances of hyperspectral imaging technology and applications in agriculture
Remote sensing is a useful tool for monitoring spatio-temporal variations of crop
morphological and physiological status and supporting practices in precision farming. In …
morphological and physiological status and supporting practices in precision farming. In …
Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry
Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be
useful in many agroforestry applications. However, it lacks the spectral range and precision …
useful in many agroforestry applications. However, it lacks the spectral range and precision …
Deep neural networks-based relevant latent representation learning for hyperspectral image classification
The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …
space, with large number of spectral bands, and low number of labeled training samples. To …
Regularizing hyperspectral and multispectral image fusion by CNN denoiser
Hyperspectral image (HSI) and multispectral image (MSI) fusion, which fuses a low-spatial-
resolution HSI (LR-HSI) with a higher resolution multispectral image (MSI), has become a …
resolution HSI (LR-HSI) with a higher resolution multispectral image (MSI), has become a …
Spectral–spatial residual network for hyperspectral image classification: A 3-D deep learning framework
In this paper, we designed an end-to-end spectral-spatial residual network (SSRN) that
takes raw 3-D cubes as input data without feature engineering for hyperspectral image …
takes raw 3-D cubes as input data without feature engineering for hyperspectral image …
Few-shot hyperspectral image classification with unknown classes using multitask deep learning
Current hyperspectral image classification assumes that a predefined classification system
is closed and complete, and there are no unknown or novel classes in the unseen data …
is closed and complete, and there are no unknown or novel classes in the unseen data …
Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network
Recently, the rapid development of deep learning has greatly improved the performance of
image classification. However, a central problem in hyperspectral image (HSI) classification …
image classification. However, a central problem in hyperspectral image (HSI) classification …
Hyperspectral band selection: A review
A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …
using hundreds of narrow bands; this technology is used in many real-world applications …
Hyperspectral image classification with convolutional neural network and active learning
Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …
classification recently. However, its success is greatly attributed to numerous labeled …
Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …
provided end users with rich spectral, spatial, and temporal information. They have made a …