Dynamic Time Warping for Crops Mapping

Belgiu, Mariana; Zhou, Yibo; Marshall, Michael; Stein, Alfred

doi:10.5194/isprs-archives-xliii-b3-2020-947-2020

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…TWDTW process also generated longer computation time due to the O (n ˆ2) complexity problem from calculating the per-pixel distance to the training areas, which requires parallel/multi-core process to fasten the analysis [46]. In addition, the classification of TWDTW does not consider the phenology of crops (seasonality and duration) [47]; instead, it relies on the ability from the temporal spectral values from the training areas to classify objects. Therefore, improvement for TWDTW can be made by incorporating the crop phenology information to the weight [48], or by using the modified fuzzy TWDTW classification to account for the uncertainty in the classification process [49].…”

Section: Discussionmentioning

confidence: 99%

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Rafif

Kusuma

Saringatin

et al. 2021

Land

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Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; however, this application is limited when using medium to coarse resolution data. This study aims to use 3.7 m-PlanetScope™ Dove constellation data, which provides daily observations, to map crop intensity information for agricultural land in Magelang District, Indonesia. Two-stage histogram matching, before and after the monthly median composites, is used to normalize the PlanetScope data and to generate monthly data to map crop intensity information. Several methods including Time-Weighted Dynamic Time Warping (TWDTW) and the machine-learning algorithms: Random Forest (RF), Extremely Randomized Trees (ET), and Extreme Gradient Boosting (XGB) are employed in this study, and the results are validated using field survey data. Our results show that XGB generated the highest overall accuracy (OA) (95 ± 4%), followed by RF (92 ± 5%), ET (87 ± 6%), and TWDTW (81 ± 8%), for mapping four-classes of cropping intensity, with the near-infrared (NIR) band being the most important variable for identifying cropping intensity. This study demonstrates the potential of PlanetScope data for the production of cropping intensity maps at detailed resolutions.

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Section: Discussionmentioning

confidence: 99%

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Rafif

Kusuma

Saringatin

et al. 2021

Land

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“…Compared with other similarity evaluation methods, DTW overcomes the scale displacement problem to a certain extent, solves the matching problem for unequal-length time series, and can mitigate the effects of outliers to achieve an enhanced matching result for similar features. In recent years, the DTW algorithm has been combined with optical remote sensing vegetation indices and applied for the classification of remote sensing images and the classification and extraction of vegetation or land cover [45], [46], [47].…”

Section: Introductionmentioning

confidence: 99%

The Application of Compact Polarization Decomposition in the Construction of a Dual-Polarization Radar Index and the Effect Evaluation of Rape Extraction

Liang

Zhao

Tan

et al. 2023

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

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The rape planting plots are fragmented and have the same temporal variation characteristics as other land types, the rape planting area is cloudy and rainy, and the optical remote sensing images are limited, all make it difficult to extract the rape planting areas. Aiming at the above-mentioned problems, this study used microwave remote sensing data combined with the compact polarimetric technique to construct a dual-polarization radar index to classify rape planting areas and analyzed the influence of time series combinations on the extraction accuracy of rape in South China. First, based on m-χ compact polarimetric decomposition, a dual-polarization radar vegetation index (RVI) was constructed. Then, based on Sentinel-1 data, the dynamic time warping (DTW) threshold classification method was used to extract rape planting areas in six counties in the main rape-producing areas in the middle and lower reaches of the Yangtze River, China. Finally, a random forest algorithm was used to analyze and screen the optimal time series combination of the extracted regional rape planting areas. Among the 94 rape samples obtained in the field, 74 samples were accurately classified as rape, and the overall accuracy was 78.72%. Six ground samples were used to verify the accuracy of the rape planting area extraction results, and the F-1 score was 81.00%. The above-mentioned results indicated that the rape planting area extraction approach based on the RVI m-χ and DTW threshold classification methods yields high accuracy in regional rape planting

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“…Our approach is to use agglomerative hierarchical clustering with a dynamic time warping (DTW) distance to define similarity between series from different participants. DTW is a popular approach for creating clusters based on patterns over a time series, and has been used to recognize speech patterns, predict stock price, monitor crop dynamics for industrial farms, and monitor wear time of medical interventions in sleep apnea (Belgiu et al., 2020; Berndt & Clifford, 1994; Bottaz‐Bosson et al., 2021; Juang, 1984). In Figure 1, participants 51 and 58 have similar patterns of breaks, or “vacations,” during the study.…”

Section: Introductionmentioning

confidence: 99%

Pattern-Based Clustering of Daily Weigh-In Trajectories Using Dynamic Time Warping

et al. 2022

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Abstract“Smart”‐scales are a new tool for frequent monitoring of weight change as well as weigh‐in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18‐month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh‐in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh‐in by time‐invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh‐in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.

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Dynamic Time Warping for Crops Mapping

Cited by 11 publications

References 29 publications

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

The Application of Compact Polarization Decomposition in the Construction of a Dual-Polarization Radar Index and the Effect Evaluation of Rape Extraction

Pattern-Based Clustering of Daily Weigh-In Trajectories Using Dynamic Time Warping

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