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 …
COVID-19 chest X-ray image classification in the presence of noisy labels
X Ying, H Liu, R Huang - Displays, 2023 - Elsevier
Abstract The Corona Virus Disease 2019 (COVID-19) has been declared a worldwide
pandemic, and a key method for diagnosing COVID-19 is chest X-ray imaging. The …
pandemic, and a key method for diagnosing COVID-19 is chest X-ray imaging. The …
Label noise modeling and correction via loss curve fitting for SAR ATR
C Wang, J Shi, Y Zhou, L Li, X Yang… - … on Geoscience and …, 2021 - ieeexplore.ieee.org
The success of deep learning in synthetic aperture radar (SAR) automatic target recognition
(ATR) relies on a large number of labeled samples; however, there are often wrong (noisy) …
(ATR) relies on a large number of labeled samples; however, there are often wrong (noisy) …
[HTML][HTML] A novel double ensemble algorithm for the classification of multi-class imbalanced hyperspectral data
The class imbalance problem has been reported to exist in remote sensing and hinders the
classification performance of many machine learning algorithms. Several technologies, such …
classification performance of many machine learning algorithms. Several technologies, such …
A heterogeneous double ensemble algorithm for soybean planting area extraction in Google Earth Engine
Soybeans are one of the main crops grown in the United States. It is crucial to grasp the
distribution of soybean cultivation areas for ensuring food security, eradicating hunger and …
distribution of soybean cultivation areas for ensuring food security, eradicating hunger and …
Detection of Unit of Measure Inconsistency in gas turbine sensors by means of Support Vector Machine classifier
L Manservigi, D Murray, JA de la Iglesia, GF Ceschini… - ISA transactions, 2022 - Elsevier
The reliability of gas turbine diagnostics clearly relies on reliable measurements. However,
raw data reliability can be corrupted by label noise issues, as for instance an erroneous …
raw data reliability can be corrupted by label noise issues, as for instance an erroneous …
An interpretable method for identifying mislabeled commercial building based on temporal feature extraction and ensemble classifier
Proper building categorization is important in building energy efficiency analysis. Primary
space usage (PSU) is a typical and widely used commercial building categorization method …
space usage (PSU) is a typical and widely used commercial building categorization method …
Subfeature ensemble-based hyperspectral anomaly detection algorithm
Hyperspectral images (HSIs) have always played an important role in remote sensing
applications. Anomaly detection has become a hot spot in HSI processing in recent years …
applications. Anomaly detection has become a hot spot in HSI processing in recent years …
Label noise correction for crowdsourcing using dynamic resampling
Crowdsourcing provides a cost-effective labeling solution for the acquisition of labeled
training samples for machine learning by employing workers on the Internet. A common …
training samples for machine learning by employing workers on the Internet. A common …
[HTML][HTML] Spectral-Spatial Feature Enhancement Algorithm for Nighttime Object Detection and Tracking
Object detection and tracking has always been one of the important research directions in
computer vision. The purpose is to determine whether the object is contained in the input …
computer vision. The purpose is to determine whether the object is contained in the input …