A new fuzzy measurement approach for automatic change detection using remotely sensed images. (October 2018)

• Record Type: Journal Article
• Authors: Sadeghi, Vahid; Farnood Ahmadi, Farshid; Ebadi, Hamid
• Is Part Of: Measurement. Volume 127(2018)
• Page Start: 1
• Page End: 14
• Publication Date: 2018-10
• Subjects: Automatic -- Change detection -- Fuzzy measurement -- Remote sensing; ADI absolute difference image -- ANN artificial neural networks -- BCM binary change map -- CD change detection -- DI difference image -- DN digital number -- FPOC fuzzy possibility of change -- LC land cover -- LU land use -- LT lower threshold -- MF membership function -- NegC negative-change -- NC no-change -- PozC positive-change -- RCSS radiometric control set samples -- RRN relative radiometric normalization -- RS remote sensing -- TCCM three-class change map -- UT upper threshold; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.05.097
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: This paper presents a new fuzzy change detection and measurement approach to overcome the drawbacks of traditional thresholding methods in remote sensing. The proposed technique is taking the advantages of following concepts: (1) asymmetric thresholding in order to improve the reliability and accuracy of change detection in each spectral bands, (2) fuzzy measurement approach to consider ambiguity in thresholding of difference image, and represent the changes in fuzzy form and fusing the obtained change maps from various spectral bands, and (3) non-linear modeling with artificial neural networks for relative radiometric normalization (RRN). The performance of developed technique is evaluated on a pair of Landsat 5, 7 images which were taken over the southern part of Urmia Lake, Iran. The major merits of developed technique compared to prevalent thresholding techniques are: higher accuracy in change detection and measurement, higher capability in detection of multiple changes and lower dependence on quality of RRN process.