scholarly journals Heterogeneous information fusion: Combination of multiple supervised and unsupervised classification methods based on belief functions

2021 ◽  
Vol 544 ◽  
pp. 238-265
Author(s):  
Na Li ◽  
Arnaud Martin ◽  
Rémi Estival
Keyword(s):  
Information Fusion ◽  
Unsupervised Classification ◽  
Belief Functions ◽  
Classification Methods ◽  
Heterogeneous Information

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

scholarly journals The Application of Heterogeneous Information Fusion in Misalignment Fault Diagnosis of Wind Turbines

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1655 ◽  
Author(s):  
Yancai Xiao ◽  
Yujia Wang ◽  
Zhengtao Ding
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Wind Turbines ◽  
Heterogeneous Information ◽  
Misalignment Fault

scholarly journals Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data

2020 ◽  
Vol 12 (3) ◽  
pp. 759
Author(s):  
Jūratė Sužiedelytė Visockienė ◽  
Eglė Tumelienė ◽  
Vida Maliene
Keyword(s):  
Remote Sensing ◽  
Invasive Plant ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Economic Losses ◽  
Classification Methods ◽  
Earth Remote Sensing ◽  
Heracleum Sosnowskyi

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper.

scholarly journals Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

2020 ◽  
Vol 11 ◽  
Author(s):  
Chunyu Wang ◽  
Jie Zhang ◽  
Xueping Wang ◽  
Ke Han ◽  
Maozu Guo
Keyword(s):  
Information Fusion ◽  
Gene Prediction ◽  
Prediction Algorithm ◽  
Heterogeneous Information ◽  
Pathogenic Gene

Web page and image semi-supervised classification with heterogeneous information fusion

2013 ◽  
Vol 39 (3) ◽  
pp. 289-306 ◽  
Author(s):  
Youtian Du ◽  
Chang Su ◽  
Zhongmin Cai ◽  
Xiaohong Guan
Keyword(s):  
Information Fusion ◽  
Supervised Classification ◽  
Web Page ◽  
Heterogeneous Information
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