Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
نویسندگان
چکیده
منابع مشابه
Time-series averaging using constrained dynamic time warping with tolerance
In this paper, we propose an innovative averaging of a set of time-series based on the Dynamic Time Warping (DTW). The DTW is widely used in data mining since it provides not only a similarity measure, but also a temporal alignment of time-series. However, its use is often restricted to the case of a pair of signals. In this paper, we propose to extend its application to a set of signals by pro...
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Dynamic Time Warping (DTW) has been widely used in time series domain as a distance function for similarity search. Several works have utilized DTW to improve the classification accuracy as it can deal with local time shiftings in time series data by non-linear warping. However, some types of time series data do have several segments that one segment should not be compared to others even though...
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A large number of killer whale sounds have recently been classified perceptually into Call Types. [A. Hodgins-Davis, thesis, Wellesley College (2004)]. The repetition rate of the pulsed component of five or more examples of each call type has been calculated using a modified form of the FFT based comb-filter method. A dissimilarity or distance matrix for these sounds was calculated using dynami...
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A set of killer whale sounds from Marineland were recently classified automatically [Brown et al., J. Acoust. Soc. Am. 119, EL34-EL40 (2006)] into call types using dynamic time warping (DTW), multidimensional scaling, and kmeans clustering to give near-perfect agreement with a perceptual classification. Here the effectiveness of four DTW algorithms on a larger and much more challenging set of c...
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Dynamic Time Warping (DTW) is a widely used technique for univariate time series comparison. This paper proposes a new algorithm for the comparison of multivariate time series which generalize DTW for the needs of correlated multivariate time series.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2019
ISSN: 2072-4292
DOI: 10.3390/rs11101257