scholarly journals Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 77
Author(s):  
Tsu Chiang Lei ◽  
Shiuan Wan ◽  
You Cheng Wu ◽  
Hsin-Ping Wang ◽  
Chia-Wen Hsieh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Decision Tree ◽  
Data Fusion ◽  
Dynamic Time Warping ◽  
Image Data ◽  
Synthetic Aperture ◽  
Time Warping ◽  
Radar Images ◽  
Dynamic Time

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.

scholarly journals Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3851 ◽  
Author(s):  
Pei Shi ◽  
Guanghui Li ◽  
Yongming Yuan ◽  
Liang Kuang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Dynamic Time Warping ◽  
Wireless Sensor ◽  
Time Warping ◽  
Segmentation Strategy ◽  
Time Series Segmentation ◽  
Dynamic Time

For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application.

scholarly journals Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth

2018 ◽  
Vol 7 (11) ◽  
pp. 420 ◽  
Author(s):  
Christos Vasilakos ◽  
George E. Tsekouras ◽  
Palaiologos Palaiologou ◽  
Kostas Kalabokidis
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Multidimensional Scaling ◽  
Fuzzy Clustering ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Series Analysis ◽  
Modis Evi ◽  
Dynamic Time

The time-series analysis of multi-temporal satellite data is widely used for vegetation regrowth after a wildfire event. Comparisons between pre- and post-fire conditions are the main method used to monitor ecosystem recovery. In the present study, we estimated wildfire disturbance by comparing actual post-fire time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and simulated MODIS EVI based on an artificial neural network assuming no wildfire occurrence. Then, we calculated the similarity of these responses for all sampling sites by applying a dynamic time warping technique. Finally, we applied multidimensional scaling to the warping distances and an optimal fuzzy clustering to identify unique patterns in vegetation recovery. According to the results, artificial neural networks performed adequately, while dynamic time warping and the proposed multidimensional scaling along with the optimal fuzzy clustering provided consistent results regarding vegetation response. For the first two years after the wildfire, medium-high- to high-severity burnt sites were dominated by oaks at elevations greater than 200 m, and presented a clustered (predominant) response of revegetation compared to other sites.

scholarly journals Enhancement of DTW Algorithm in on-Line Signature Mapping Using the Lost-Box Recovery Algorithm

2012 ◽  
Vol 1 (2) ◽  
pp. 105-122
Author(s):  
Mohd Taib Wahid Mohd Taib Wahid
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Image Data ◽  
Time Warping ◽  
Recovery Algorithm ◽  
On Line ◽  
Dynamic Time

Signature is a common practice as a way of identifies authentication or verification. Signature On-line validation and authentication is a field of authenticating the time series signature data which is generally acquired from the tablet-based device. Different from common traditional signature image, the on-line signature image data contain number of points that are arranged in sequence of time. The aim of this research is to enhance existing approach to map the strokes in both test and reference signatures. Current methods make use of the DTW (Dynamic Time Warping) algorithm and its deviate to segment them before comparing each of its data dimension. This study suggesting a modified DTW algorithm with the proposed Missed Nodes Recovery Algorithm aims to improve the mapping performance.

scholarly journals New DTW Windows Type for Forward- and Backward-Lookingness Examination. Application for Inflation Expectation

2021 ◽  
Author(s):  
Aleksandra Rutkowska ◽  
Magdalena Szyszko
Keyword(s):  
Time Series ◽  
Information Content ◽  
Dynamic Time Warping ◽  
The Novel ◽  
Time Warping ◽  
Specific Question ◽  
Inflation Expectation ◽  
Standard Tool ◽  
Dynamic Time ◽  
Research Period

AbstractThis study provides an application of dynamic time warping algorithm with a new window constraint to assess consumer expectations’ information content regarding current and future inflation. Our study’s contribution is the novel application of DTW for testing expectations’ forward-lookingness. Additionally, we modify the algorithm to adjust it for a specific question on the information content of our data. The DTW overcomes constraints of the standard tool that examines forward-lookingness: DTW does not impose assumptions on time series properties. In empirical study we cover seven European counties and compare the DTW outcomes with the results of previous studies in these economies using a standard methodology. The research period covers 2001 to mid-2018. Application of DTW provides information on the degree of expectations’ forward-lookingness. The result, after standardization, are similar to the standard parameters of hybrid specification of expectations. Moreover, the rankings of most forward-looking consumers are replicated. Our results confirm the economic intuition, and they do not contradict previous studies.

scholarly journals The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4024
Author(s):  
Krzysztof Dmytrów ◽  
Joanna Landmesser ◽  
Beata Bieszk-Stolorz
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Dynamic Time Warping ◽  
Global Economy ◽  
Price Change ◽  
Commodity Prices ◽  
The Other ◽  
Time Warping ◽  
Heating Oil ◽  
Dynamic Time

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

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