Evidential Reasoning Approach to Multisource-Data Classification in Remote Sensing

Hakil Kim; Philip H. Swain

doi:10.1109/21.398687

Evidential Reasoning Approach to Multisource-Data Classification in Remote Sensing

Hakil Kim, Philip H. Swain

Lafayette, IN, USA

Research output: Contribution to journal › Article › peer-review

40 Scopus citations

Abstract

In the evidential reasoning approach to the classification of remotely sensed multisource data, each data source is considered as providing a body of evidence with a certain degree of belief. The degrees of belief are represented by “interval-valued probabilities” rather than by conventional point-valued probabilities so that uncertainty can be embedded in the measures. The proposed method is applied to the ground-cover classification of simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data, from which a set of multiple sources is obtained by dividing the dimensionally huge data into smaller pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than conventional maximum likelihood methods.

Original language	English
Pages (from-to)	1257-1265
Number of pages	9
Journal	IEEE Transactions on Systems, Man and Cybernetics
Volume	25
Issue number	8
DOIs	https://doi.org/10.1109/21.398687
State	Published - Aug 1995

Bibliographical note

Funding Information:
Manuscript received October 10, 1993; revised September 17, 1994. This work was supported in part by NASA Contract NAGW-925. H. Kim is with the Department of Automation Engineering, INHA University, Inchon, Korea 402-7.5 1. P. H. Swain is with the School of Electrical Engineering, Purdue University, West Lafayette, IN 47907 USA. IEEE Log Number 9411912.

Access to Document

10.1109/21.398687

Cite this

@article{a3ac9232cbaf444c9de8278994e6f2c9,

title = "Evidential Reasoning Approach to Multisource-Data Classification in Remote Sensing",

abstract = "In the evidential reasoning approach to the classification of remotely sensed multisource data, each data source is considered as providing a body of evidence with a certain degree of belief. The degrees of belief are represented by “interval-valued probabilities” rather than by conventional point-valued probabilities so that uncertainty can be embedded in the measures. The proposed method is applied to the ground-cover classification of simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data, from which a set of multiple sources is obtained by dividing the dimensionally huge data into smaller pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than conventional maximum likelihood methods.",

author = "Hakil Kim and Swain, {Philip H.}",

note = "Funding Information: Manuscript received October 10, 1993; revised September 17, 1994. This work was supported in part by NASA Contract NAGW-925. H. Kim is with the Department of Automation Engineering, INHA University, Inchon, Korea 402-7.5 1. P. H. Swain is with the School of Electrical Engineering, Purdue University, West Lafayette, IN 47907 USA. IEEE Log Number 9411912.",

year = "1995",

month = aug,

doi = "10.1109/21.398687",

language = "English",

volume = "25",

pages = "1257--1265",

journal = "IEEE Transactions on Systems, Man and Cybernetics",

issn = "0018-9472",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "8",

}

TY - JOUR

T1 - Evidential Reasoning Approach to Multisource-Data Classification in Remote Sensing

AU - Kim, Hakil

AU - Swain, Philip H.

N1 - Funding Information: Manuscript received October 10, 1993; revised September 17, 1994. This work was supported in part by NASA Contract NAGW-925. H. Kim is with the Department of Automation Engineering, INHA University, Inchon, Korea 402-7.5 1. P. H. Swain is with the School of Electrical Engineering, Purdue University, West Lafayette, IN 47907 USA. IEEE Log Number 9411912.

PY - 1995/8

Y1 - 1995/8

N2 - In the evidential reasoning approach to the classification of remotely sensed multisource data, each data source is considered as providing a body of evidence with a certain degree of belief. The degrees of belief are represented by “interval-valued probabilities” rather than by conventional point-valued probabilities so that uncertainty can be embedded in the measures. The proposed method is applied to the ground-cover classification of simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data, from which a set of multiple sources is obtained by dividing the dimensionally huge data into smaller pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than conventional maximum likelihood methods.

AB - In the evidential reasoning approach to the classification of remotely sensed multisource data, each data source is considered as providing a body of evidence with a certain degree of belief. The degrees of belief are represented by “interval-valued probabilities” rather than by conventional point-valued probabilities so that uncertainty can be embedded in the measures. The proposed method is applied to the ground-cover classification of simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data, from which a set of multiple sources is obtained by dividing the dimensionally huge data into smaller pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than conventional maximum likelihood methods.

UR - http://www.scopus.com/inward/record.url?scp=0029360016&partnerID=8YFLogxK

U2 - 10.1109/21.398687

DO - 10.1109/21.398687

M3 - Article

AN - SCOPUS:0029360016

SN - 0018-9472

VL - 25

SP - 1257

EP - 1265

JO - IEEE Transactions on Systems, Man and Cybernetics

JF - IEEE Transactions on Systems, Man and Cybernetics

IS - 8

ER -

Evidential Reasoning Approach to Multisource-Data Classification in Remote Sensing

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this