Generative adversarial minority oversampling for spectral–spatial hyperspectral image classification
Recently, convolutional neural networks (CNNs) have exhibited commendable performance
for hyperspectral image (HSI) classification. Generally, an important number of samples are …
for hyperspectral image (HSI) classification. Generally, an important number of samples are …
A review of addressing class noise problems of remote sensing classification
W Feng, Y Long, S Wang… - Journal of Systems …, 2023 - ieeexplore.ieee.org
The development of image classification is one of the most important research topics in
remote sensing. The prediction accuracy depends not only on the appropriate choice of the …
remote sensing. The prediction accuracy depends not only on the appropriate choice of the …
Iterative training sample expansion to increase and balance the accuracy of land classification from VHR imagery
Imbalanced training sets are known to produce suboptimal maps for supervised
classification. Therefore, one challenge in mapping land cover is acquiring training data that …
classification. Therefore, one challenge in mapping land cover is acquiring training data that …
Enhanced-random-feature-subspace-based ensemble CNN for the imbalanced hyperspectral image classification
Hyperspectral image (HSI) classification often faces the problem of multiclass imbalance,
which is considered to be one of the major challenges in the field of remote sensing. In …
which is considered to be one of the major challenges in the field of remote sensing. In …
A novel image fusion method of multi-spectral and sar images for land cover classification
The fusion of multi-spectral and synthetic aperture radar (SAR) images could retain the
advantages of each data, hence benefiting accurate land cover classification. However …
advantages of each data, hence benefiting accurate land cover classification. However …
A new approach for the detection of abnormal heart sound signals using TQWT, VMD and neural networks
Phonocardiogram (PCG) plays an important role in evaluating many cardiac abnormalities,
such as the valvular heart disease, congestive heart failure and anatomical defects of the …
such as the valvular heart disease, congestive heart failure and anatomical defects of the …
Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data
In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for
the classification of hyperspectral images with limited training data. Our proposition is based …
the classification of hyperspectral images with limited training data. Our proposition is based …
RUESVMs: An ensemble method to handle the class imbalance problem in land cover mapping using Google Earth Engine
Timely and accurate Land Cover (LC) information is required for various applications, such
as climate change analysis and sustainable development. Although machine learning …
as climate change analysis and sustainable development. Although machine learning …
A pixel cluster CNN and spectral-spatial fusion algorithm for hyperspectral image classification with small-size training samples
Convolutional neural networks (CNNs) can automatically learn features from the
hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features …
hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features …
A hybrid data balancing method for classification of imbalanced training data within google earth engine: Case studies from mountainous regions
A Naboureh, A Li, J Bian, G Lei, M Amani - Remote Sensing, 2020 - mdpi.com
Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes
dominating against minority classes in mountainous areas. Although standard Machine …
dominating against minority classes in mountainous areas. Although standard Machine …