Results 51 to 60 of about 2,740,047 (370)

Unsupervised PolSAR Image Classification Based on Superpixel Pseudo-Labels and a Similarity-Matching Network

Remote Sensing
Supervised polarimetric synthetic aperture radar (PolSAR) image classification demands a large amount of precisely labeled data. However, such data are difficult to obtain.
Lei Wang, Lingmu Peng, Rong Gui, Hanyu Hong, Shenghui Zhu   +4 more
doaj   +1 more source

A Comparative Study on Classification Features between High-Resolution and Polarimetric SAR Images through Unsupervised Classification Methods

Remote Sensing, 2022
Feature extraction and comparison of synthetic aperture radar (SAR) data of different modes such as high resolution and full polarization have important guiding significance for SAR image applications.
Junrong Qu, Xiaolan Qiu, Wei Wang, Zezhong Wang, Bin Lei, Chibiao Ding   +5 more
doaj   +1 more source

A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameter-Based Unsupervised Classification Scheme Using a Geodesic Distance [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2019
We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain $N$ scattering power components along with a residue power component for each pixel.
D. Ratha, E. Pottier, A. Bhattacharya, A. Frery   +3 more
semanticscholar   +1 more source

Unsupervised star, galaxy, QSO classification [PDF]

Astronomy & Astrophysics, 2020
Context. Classification will be an important first step for upcoming surveys aimed at detecting billions of new sources, such as LSST and Euclid, as well as DESI, 4MOST, and MOONS. The application of traditional methods of model fitting and colour-colour selections will face significant computational constraints, while machine-learning methods offer a ...
C. H. A. Logan, S. Fotopoulou
openaire   +5 more sources

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering [PDF]

, 2014
Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic
Barrick, TR, Howe, FA, Raschke, F, Yang, G   +3 more
core   +1 more source

Unsupervised classification of noisy chromosomes [PDF]

Bioinformatics, 2001
Abstract Motivation: Almost all methods of chromosome recognition assume supervised training; i.e. we are given correctly classified chromosomes to start the training phase. Noise, if any, is confined only in the representation of the chromosomes and not in the classification of the chromosomes.
openaire   +2 more sources

An Unsupervised Classification Method for Flame Image of Pulverized Coal Combustion Based on Convolutional Auto-Encoder and Hidden Markov Model

Energies, 2019
Combustion condition monitoring is a fundamental and critical issue that needs to be addressed in the wide-load operation of coal-fired boilers. In this paper, an unsupervised classification framework based on the convolutional auto-encoder (CAE), the ...
Tian Qiu, Minjian Liu, Guiping Zhou, Li Wang, K. Gao   +4 more
semanticscholar   +1 more source

CNN features are also great at unsupervised classification [PDF]

arXiv.org, 2017
This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the problem of image set clustering for object classification as well as fine-
Joris Guérin, O. Gibaru, Stéphane Thiery, E. Nyiri   +3 more
semanticscholar   +1 more source

Adaptaquin is selectively toxic to glioma stem cells through disruption of iron and cholesterol metabolism

Molecular Oncology, EarlyView.
Adaptaquin selectively kills glioma stem cells while sparing differentiated brain cells. Transcriptomic and proteomic analyses show Adaptaquin disrupts iron and cholesterol homeostasis, with iron chelation amplifying cytotoxicity via cholesterol depletion, mitochondrial dysfunction, and elevated reactive oxygen species.
Adrien M. Vaquié, Davod R. Shah, Eliane E. S. Brechbühl, Michael McNicholas, Zhaoyang Xu, John H. Stockley, Laura Morcom, Diana Gold Diaz, Gemma C. Girdler, Rachel V. Seear, Gabriel Balmus, Rajiv R. Ratan, Harry Bulstrode, James A. Nathan, Manav Pathania, Kevin M. Brindle, David H. Rowitch   +16 more
wiley   +1 more source

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

Remote Sensing, 2018
When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery ...
Cong Wang, Lei Zhang, Wei Wei, Yanning Zhang   +3 more
doaj   +1 more source