Land–Use and Land-Cover Change Detection Using Dynamic Time Warping–Based Time Series Clustering Method

Zhang, Yanghua; Zhao, Hu

doi:10.1080/07038992.2020.1740083

Cited by 16 publications

(9 citation statements)

References 63 publications

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“…Wang et al [36] applied time series NDVI to establish hypothesized trajectories of impervious surface expansion and analyzed trajectory features using three phases and four types with nine parameters. Besides the above inter-annual temporal trajectories, Zhang and Zhao [37] developed the intra-annual trajectories of BCI and NDVI over a two-year period to detect urban land cover changes, and some changes that are difficult to extract by bi-temporal images were detected.…”

Section: Related Workmentioning

confidence: 99%

A Time-Series Approach to Detect Urbanized Areas Using Biophysical Indicators and Landsat Satellite Imagery

Zhang

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Related Workmentioning

confidence: 99%

A Time-Series Approach to Detect Urbanized Areas Using Biophysical Indicators and Landsat Satellite Imagery

Zhang

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The trend and fluctuations of the sequence can be judged by the sequence shape, hence this study used Dynamic time warping (DTW) is one of the most wellknown pattern match techniques to determine the shape similarity between two time series [37]. Compared with other pattern match methods, DTW is a simple but effective method that has been applied in many fields [38], such as speech recognition [39,40], computer vision and process monitoring [41,42]. Based on a dynamic programming technique, DTW could find the minimal distance between two time series by shrinking or stretching its time dimension [33,43].…”

Section: Instance-based Transfer With Minimum Dtw Distancementioning

confidence: 99%

Deep transfer learning: a novel glucose prediction framework for new subjects with type 2 diabetes

Xia

Yang

et al. 2021

Complex Intell. Syst.

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Blood glucose (BG) prediction is an effective approach to avoid hyper- and hypoglycemia, and achieve intelligent glucose management for patients with type 1 or serious type 2 diabetes. Recent studies have tended to adopt deep learning networks to obtain improved prediction models and more accurate prediction results, which have often required significant quantities of historical continuous glucose-monitoring (CGM) data. However, for new patients with limited historical dataset, it becomes difficult to establish an acceptable deep learning network for glucose prediction. Consequently, the goal of this study was to design a novel prediction framework with instance-based and network-based deep transfer learning for cross-subject glucose prediction based on segmented CGM time series. Taking the effects of biodiversity into consideration, dynamic time warping (DTW) was applied to determine the proper source domain dataset that shared the greatest degree of similarity for new subjects. After that, a network-based deep transfer learning method was designed with cross-domain dataset to obtain a personalized model combined with improved generalization capability. In a case study, the clinical dataset demonstrated that, with additional segmented dataset from other subjects, the proposed deep transfer learning framework achieved more accurate glucose predictions for new subjects with type 2 diabetes.

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“…In recent decades, remote sensing data have been widely used in urbanization process studies because, in comparison with census data, they can provide timely and spatially explicit information [ 6 , 7 ]. Frequently used remote sensing data include multispectral reflectance [ 8 , 9 ], normalized difference vegetation index (NDVI) [ 10 , 11 ], nighttime lights data (NTL) [ 7 , 12 ], the biophysical composition index (BCI) [ 13 ] and other indicators that reflect urban land types [ 14 ]. Most of these data have been used for detection of urban land use change.…”

Section: Introductionmentioning

confidence: 99%

Urban development trend analysis and spatial simulation based on time series remote sensing data: A case study of Jinan, China

Zhang

Zhao

et al. 2021

PLoS ONE

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Uncontrolled urban growth detracts from healthy urban development. Understanding urban development trends and predicting future urban spatial states is of great practical significance. In order to comprehensively analyze urbanization and its effect on vegetation cover, we extracted urban development trends from time series DMSP/OLS NTL and NDVI data from 2000 to 2015, using a linear model fitting method. Six urban development trend types were identified by clustering the linear model parameters. The identified trend types were found to accurately reflect the on-ground conditions and changes in the Jinan area. For example, a high-density, stable urban type was found in the city center while a stable dense vegetation type was found in the mountains to the south. The SLEUTH model was used for urban growth simulation under three scenarios built on the urban development analysis results. The simulation results project a gentle urban growth trend from 2015 to 2030, demonstrating the prospects for urban growth from the perspective of environmental protection and conservative urban development.

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Land–Use and Land-Cover Change Detection Using Dynamic Time Warping–Based Time Series Clustering Method

Cited by 16 publications

References 63 publications

A Time-Series Approach to Detect Urbanized Areas Using Biophysical Indicators and Landsat Satellite Imagery

A Time-Series Approach to Detect Urbanized Areas Using Biophysical Indicators and Landsat Satellite Imagery

Deep transfer learning: a novel glucose prediction framework for new subjects with type 2 diabetes

Urban development trend analysis and spatial simulation based on time series remote sensing data: A case study of Jinan, China

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