Hyperspectral image classification via multiple-feature-based adaptive sparse representation
A multiple-feature-based adaptive sparse representation (MFASR) method is proposed for
the classification of hyperspectral images (HSIs). The proposed method mainly includes the …
the classification of hyperspectral images (HSIs). The proposed method mainly includes the …
Hyperspectral band selection by multitask sparsity pursuit
Y Yuan, G Zhu, Q Wang - IEEE Transactions on Geoscience …, 2014 - ieeexplore.ieee.org
Hyperspectral images have been proved to be effective for a wide range of applications;
however, the large volume and redundant information also bring a lot of inconvenience at …
however, the large volume and redundant information also bring a lot of inconvenience at …
Fast hyperspectral anomaly detection via high-order 2-D crossing filter
Y Yuan, Q Wang, G Zhu - IEEE Transactions on Geoscience …, 2014 - ieeexplore.ieee.org
Anomaly detection has been an important topic in hyperspectral image analysis. This
technique is sometimes more preferable than the supervised target detection because it …
technique is sometimes more preferable than the supervised target detection because it …
Feature extraction via joint adaptive structure density for hyperspectral imagery classification
Feature extraction is known to be an effective way in both reducing computational
complexity and increasing accuracy in hyperspectral imagery (HSI) classification. In this …
complexity and increasing accuracy in hyperspectral imagery (HSI) classification. In this …
Multiview-based random rotation ensemble pruning for hyperspectral image classification
Y Zhang, G Cao, X Li - IEEE Transactions on Instrumentation …, 2020 - ieeexplore.ieee.org
Ensembles of extreme learning machine (ELM) have been widely used for hyperspectral
image classification. The previous studies have shown that the goal of ensemble learning is …
image classification. The previous studies have shown that the goal of ensemble learning is …
Spatially regularized semisupervised ensembles of extreme learning machines for hyperspectral image segmentation
This paper explores the performance of Ensembles of Extreme Learning Machine classifiers
for hyperspectral image classification and segmentation in a semisupervised and spatially …
for hyperspectral image classification and segmentation in a semisupervised and spatially …
Image understanding applications of lattice autoassociative memories
Multivariate mathematical morphology (MMM) aims to extend the mathematical morphology
from gray scale images to images whose pixels are high-dimensional vectors, such as …
from gray scale images to images whose pixels are high-dimensional vectors, such as …
Optimized ensemble EMD-based spectral features for hyperspectral image classification
Z He, Y Shen, Q Wang, Y Wang - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
Extracting essential features from massive bands is an important yet challenging issue in
hyperspectral image (HSI) classification. Plenty of feature extraction techniques can be …
hyperspectral image (HSI) classification. Plenty of feature extraction techniques can be …
Spectral-spatial hyperspectral image classification via SVM and superpixel segmentation
Z He, Y Shen, M Zhang, Q Wang… - 2014 IEEE …, 2014 - ieeexplore.ieee.org
Integration of spatial information has recently emerged as a powerful tool in improving the
classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous …
classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous …
Applications of hybrid extreme rotation forests for image segmentation
This paper introduces the Hybrid Extreme Rotation Forest (HERF) classifier describing two
succesful applications in the image segmentation domain. The HERF is an ensemble of …
succesful applications in the image segmentation domain. The HERF is an ensemble of …