Results 21 to 30 of about 53,858 (186)

An Adaptive Semi-Parametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images [PDF]

open access: yes, 2002
In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in ...
Bruzzone, Lorenzo   +1 more
core   +1 more source

Data-Mining a Large Digital Sky Survey: From the Challenges to the Scientific Results [PDF]

open access: yes, 1997
The analysis and an efficient scientific exploration of the Digital Palomar Observatory Sky Survey (DPOSS) represents a major technical challenge. The input data set consists of 3 Terabytes of pixel information, and contains a few billion sources.
de Carvalho, R. R.   +6 more
core   +3 more sources

A Local Potential-Based Clustering Algorithm for Unsupervised Hyperspectral Band Selection

open access: yesIEEE Access, 2019
Unsupervised band selection plays an increasingly important role in a hyperspectral image (HSI) classification because of inadequate labeling samples.
Zhaokui Li   +5 more
doaj   +1 more source

Unsupervised Hyperspectral Band Selection Using Graphics Processing Units

open access: yesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011
The high dimensionality of hyperspectral imagery challenges image processing and analysis. Band selection is a common technique for dimensionality reduction. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands.
He Yang, Qian Du, Genshe Chen
openaire   +1 more source

Methodology for determining deforestation areas in Lviv region using remote sensing data [PDF]

open access: yesAdvances in Geodesy and Geoinformation, 2022
The object of the study is the processing of space images on the territory of the Carpathian territory in the Lviv region, obtained from the Landsat-8 satellite. The work aims to determine the area of deforestation in the Carpathian territory of the Lviv
Borys Chetverikov   +3 more
doaj   +1 more source

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images [PDF]

open access: yes, 2018
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA
Abdel-Rahman, Mohamed H.   +740 more
core   +3 more sources

Unsupervised Band Selection for Hyperspectral Imagery Classification Without Manual Band Removal

open access: yesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012
The rich information available in hyperspectral imagery has provided significant opportunities for material classification and identification. Due to the problem of the “curse of dimensionality” (called Hughes phenomenon) posed by the high number of spectral channels along with small amounts of labeled training samples, dimensionality reduction is a ...
Sen Jia   +3 more
openaire   +1 more source

Exploration of Large Digital Sky Surveys [PDF]

open access: yes, 2000
We review some of the scientific opportunities and technical challenges posed by the exploration of the large digital sky surveys, in the context of a Virtual Observatory (VO).
A. A. Mahabal   +12 more
core   +3 more sources

Optimal Clustering Framework for Hyperspectral Band Selection

open access: yes, 2018
Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been
Li, Xuelong, Wang, Qi, Zhang, Fahong
core   +1 more source

Novel hyperbolic clustering-based band hierarchy (HCBH) for effective unsupervised band selection of hyperspectral images

open access: yesPattern Recognition, 2022
For dimensionality reduction of HSI, many clustering-based unsupervised band selection (UBS) methods have been proposed due to their superiority of reducing the high redundancy between selected bands. However, most of these methods fail to reflect the data structure of HSI, leading to inconsistent results of band selection.
He Sun   +3 more
openaire   +2 more sources

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