Big data and machine learning with hyperspectral information in agriculture
KLM Ang, JKP Seng - IEEE Access, 2021 - ieeexplore.ieee.org
Hyperspectral and multispectral information processing systems and technologies have
demonstrated its usefulness for the improvement of agricultural productivity and practices by …
demonstrated its usefulness for the improvement of agricultural productivity and practices by …
HybridSN: Exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification
Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed
images. Hyperspectral imagery includes varying bands of images. Convolutional neural …
images. Hyperspectral imagery includes varying bands of images. Convolutional neural …
NSCKL: Normalized spectral clustering with kernel-based learning for semisupervised hyperspectral image classification
Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …
classification. However, most SSC methods mainly consider local information, so that some …
[HTML][HTML] Deep hybrid: multi-graph neural network collaboration for hyperspectral image classification
D Yao, Z Zhi-li, Z **ao-feng, C Wei, H Fang… - Defence …, 2023 - Elsevier
With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult
Problem in current research. The graph neural network (GNN) has emerged as an approach …
Problem in current research. The graph neural network (GNN) has emerged as an approach …
A supervised segmentation network for hyperspectral image classification
H Sun, X Zheng, X Lu - IEEE Transactions on Image …, 2021 - ieeexplore.ieee.org
Recently, deep learning has drawn broad attention in the hyperspectral image (HSI)
classification task. Many works have focused on elaborately designing various spectral …
classification task. Many works have focused on elaborately designing various spectral …
Learning a low tensor-train rank representation for hyperspectral image super-resolution
Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
Remote sensing scene classification using multilayer stacked covariance pooling
This paper proposes a new method, called multilayer stacked covariance pooling (MSCP),
for remote sensing scene classification. The innovative contribution of the proposed method …
for remote sensing scene classification. The innovative contribution of the proposed method …
Feature extraction with multiscale covariance maps for hyperspectral image classification
The classification of hyperspectral images (HSIs) using convolutional neural networks
(CNNs) has recently drawn significant attention. However, it is important to address the …
(CNNs) has recently drawn significant attention. However, it is important to address the …
Nearest neighboring self-supervised learning for hyperspectral image classification
Recently, state-of-the-art classification performance of natural images has been obtained by
self-supervised learning (S2L) as it can generate latent features through learning between …
self-supervised learning (S2L) as it can generate latent features through learning between …
Self-supervised learning with adaptive distillation for hyperspectral image classification
Hyperspectral image (HSI) classification is an important topic in the community of remote
sensing, which has a wide range of applications in geoscience. Recently, deep learning …
sensing, which has a wide range of applications in geoscience. Recently, deep learning …