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
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
Structured Clutter Covariance Matrix Estimation for Airborne MIMO Radar With Limited Training Data
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3027818
Abstract: This letter studies the estimation of structured clutter covariance matrix (CCM) for space–time adaptive processing (STAP)-based airborne multiin multiout (MIMO) radar with limited training data. The Kronecker-product-expansion structure of the CCM is considered, where each… read more here.
Keywords: limited training; clutter covariance; structured clutter; covariance matrix ... See more keywords
Adaptive Spatial Pyramid Constraint for Hyperspectral Image Classification With Limited Training Samples
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3095056
Abstract: Deep learning-based methods have made significant progress in hyperspectral image (HSI) classification in recent years. However, deep learning-based methods usually rely on a large number of samples, and in many cases, it is difficult to… read more here.
Keywords: classification; pyramid; method; training samples ... See more keywords
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3106831
Abstract: Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene interaction and regulation in Drosophila embryogenesis,… read more here.
Keywords: biological image; training; low shot; limited training ... See more keywords
Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Plant Science"
DOI: 10.3389/fpls.2020.583438
Abstract: Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process.… read more here.
Keywords: classification; plant; limited training; based plant ... See more keywords
Data Preprocessing and Neural Network Architecture Selection Algorithms in Cases of Limited Training Sets - On an Example of Diagnosing Alzheimer's Disease
Sign Up to like & getrecommendations! Published in 2023 at "Algorithms"
DOI: 10.3390/a16050219
Abstract: This paper aimed to increase accuracy of an Alzheimer’s disease diagnosing function that was obtained in a previous study devoted to application of decision roots to the diagnosis of Alzheimer’s disease. The obtained decision root… read more here.
Keywords: limited training; neural network; network; neural networks ... See more keywords