Sparse representation-based classification using generalized weighted extended dictionary
Sign Up to like & getrecommendations! Published in 2017 at "Soft Computing"
DOI: 10.1007/s00500-016-2064-7
Abstract: Sparse representation-based classification (SRC) has been a breakthrough of face recognition and signal reconstruction recently. However, few face images from the same subject provide insufficient observations. Meanwhile, owing to uncertainty of training images with variations… read more here.
Keywords: classification; sparse representation; training; training samples ... See more keywords
Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery
Sign Up to like & getrecommendations! Published in 2018 at "Journal of the Indian Society of Remote Sensing"
DOI: 10.1007/s12524-018-0777-z
Abstract: In this study, we used Landsat-8 imagery to test object- and pixel-based image classification approaches in an urban fringe area. For object-based classification, we applied four machine learning classifiers: decision tree (DT), naive Bayes (NB),… read more here.
Keywords: effects training; classification; pixel based; training samples ... See more keywords
The migration of training samples towards dynamic global land cover mapping
Sign Up to like & getrecommendations! Published in 2020 at "Isprs Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2020.01.010
Abstract: Abstract High quality training samples are essential for global land cover mapping. Traditionally, training samples are collected by field work or via manual interpretation based on high-resolution Google Earth images. Due to the difficulty of… read more here.
Keywords: global land; training samples; training; land cover ... See more keywords
Multiple classifier systems for classification of multifrequency PolSAR images with limited training samples
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2018.1471543
Abstract: ABSTRACT The aim of this article is to improve land-cover classification accuracy from multifrequency full-polarimetric synthetic aperture radar (PolSAR) observations using multiple classifier systems (MCSs) when limited training samples are available. Two types of popular… read more here.
Keywords: based mcss; classification; polsar; training samples ... See more keywords
An improved SRC method based on virtual samples for face recognition
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Modern Optics"
DOI: 10.1080/09500340.2018.1455923
Abstract: Abstract The sparse representation classifier (SRC) performs classification by evaluating which class leads to the minimum representation error. However, in real world, the number of available training samples is limited due to noise interference, training… read more here.
Keywords: improved src; training samples; virtual samples; representation ... See more keywords
A sampling workflow based on unsupervised clusters and multi-temporal sample interpretation (UCMT) for cropland mapping
Sign Up to like & getrecommendations! Published in 2018 at "Remote Sensing Letters"
DOI: 10.1080/2150704x.2018.1500045
Abstract: ABSTRACT Accurate cropland maps are important input for various proposes, such as ecosystem service and land cover change monitoring, and the representativeness of sample training samples influence the cropland mapping accuracy significantly. This study aims… read more here.
Keywords: cropland; multi temporal; training; sampling workflow ... See more keywords
Adaptive Sample Weight for Machine Learning Computer Vision Algorithms in V2X Systems
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2018.2888969
Abstract: In machine learning, training sample set management has an important impact on the performance of visual detection and tracking algorithms, as corrupted training samples degrade the tracking performance, especially in practical scenarios such as vehicular… read more here.
Keywords: training sample; training; adaptive sample; training samples ... See more keywords
Weakly Supervised Semantic and Attentive Data Mixing Augmentation for Fine-Grained Visual Categorization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3163302
Abstract: As a key factor, the availability of large-scale training samples determines the improvement of visual performance. However, the size of Fine-Grained Visual Categorization (FGVC) datasets is always limited. Therefore, overfitting as an issue in FGVC-related… read more here.
Keywords: data mixing; training samples; training; mixing augmentation ... See more keywords
Wet-GC: A Novel Multimodel Graph Convolutional Approach for Wetland Classification Using Sentinel-1 and 2 Imagery With Limited Training Samples
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3177579
Abstract: Wetland is one of the most productive resources on earth, and it provides vital habitats for several unique species of flora and fauna. Over the last decade, mapping and monitoring wetlands by utilizing deep learning… read more here.
Keywords: limited training; graph convolutional; wetland classification; wetland ... See more keywords
Structure Preserving Transfer Learning for Unsupervised Hyperspectral Image Classification
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2017.2723763
Abstract: Recent advances on remote sensing techniques allow easier access to imaging spectrometer data. Manually labeling and processing of such collected hyperspectral images (HSIs) with a vast quantities of samples and a large number of bands… read more here.
Keywords: transfer learning; classification; training samples; transfer ... See more keywords
Hyperspectral Imagery Noisy Label Detection by Spectral Angle Local Outlier Factor
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2018.2842792
Abstract: This letter presents the hyperspectral imagery (HSI) noisy label detection using a spectral angle and the local outlier factor (SALOF) algorithm. The noisy label is caused by a mislabeled training pixel, and thus, noisy training… read more here.
Keywords: label detection; detection; training samples; spectral angle ... See more keywords