Open AccessMining change patterns in time series high‐resolution SAR images
Author(s) -
Peng Dong,
Pan Ting,
Yang Wen,
Li HengChao,
Liao Mingsheng
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0349
Subject(s) - synthetic aperture radar , series (stratigraphy) , computer science , focus (optics) , artificial intelligence , radar imaging , measure (data warehouse) , simple (philosophy) , remote sensing , high resolution , time series , computer vision , pattern recognition (psychology) , phase (matter) , radar , algorithm , data mining , geology , telecommunications , machine learning , optics , physics , paleontology , philosophy , epistemology , quantum mechanics
Here, the authors use the K‐Shape algorithm, which is based on a z ‐normalised version of cross‐correlation distance measure, to extract change patterns from time series synthetic aperture radar (SAR) images. These change patterns focus only on the shape of time series, rather than amplitude and phase. Besides, they employ a simple and elegant method to choose the optimal number of clusters. Experimental results on multi‐temporal SAR images of Shanghai indicate the effectiveness of their approach.