There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 2776 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 2761 | - |
dc.citation.title | REMOTE SENSING OF ENVIRONMENT | - |
dc.citation.volume | 112 | - |
dc.contributor.author | Im, Jungho | - |
dc.contributor.author | Jensen, John R. | - |
dc.contributor.author | Hodgson, Michael E. | - |
dc.date.accessioned | 2023-12-22T08:39:34Z | - |
dc.date.available | 2023-12-22T08:39:34Z | - |
dc.date.created | 2014-11-05 | - |
dc.date.issued | 2008-06 | - |
dc.description.abstract | Binary discriminant functions are often used to identify changed area through time in remote sensing change detection studies. Traditionally, a single change-enhanced image has been used to optimize the binary discriminant function with a few (e.g., 5-10) discrete thresholds using a trial-and-error method. Im et al. [Im, J., Rhee, J., Jensen, J. R., & Hodgson, M. E. (2007). An automated binary change detection model using a calibration approach. Remote Sensing of Environment, 106, 89-105] developed an automated calibration model for optimizing the binary discriminant function by autonomously testing thousands of thresholds. However, the automated model may be time-consuming especially when multiple change-enhanced images are used as inputs together since the model is based on an exhaustive search technique. This paper describes the development of a computationally efficient search technique for identifying optimum threshold(s) in a remote sensing spectral search space. The new algorithm is based on "systematic searching." Two additional heuristic optimization algorithms (i.e., hill climbing, simulated annealing) were examined for comparison. A case study using QuickBird and IKONOS satellite imagery was performed to evaluate the effectiveness of the proposed algorithm. The proposed systematic search technique reduced the processing time required to identify the optimum binary discriminate function without decreasing accuracy. The other two optimizing search algorithms also reduced the processing time but failed to detect a global maxima for some spectral features. | - |
dc.identifier.bibliographicCitation | REMOTE SENSING OF ENVIRONMENT, v.112, no.6, pp.2761 - 2776 | - |
dc.identifier.doi | 10.1016/j.rse.2008.01.007 | - |
dc.identifier.issn | 0034-4257 | - |
dc.identifier.scopusid | 2-s2.0-43949146640 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/8288 | - |
dc.identifier.url | http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=43949146640 | - |
dc.identifier.wosid | 000256986400003 | - |
dc.language | 영어 | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.title | Optimizing the binary discriminant function in change detection applications | - |
dc.type | Article | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | systematic search technique | - |
dc.subject.keywordAuthor | change detection | - |
dc.subject.keywordAuthor | optimization | - |
dc.subject.keywordAuthor | binary discriminant function |
- |
dc.subject.keywordAuthor | hill climbing | - |
dc.subject.keywordAuthor | simulated annealing | - |
dc.subject.keywordPlus | CORRELATION IMAGE-ANALYSIS | - |
dc.subject.keywordPlus | CHANGE VECTOR ANALYSIS | - |
dc.subject.keywordPlus | DATA ASSIMILATION | - |
dc.subject.keywordPlus | LAND-COVER | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.