MODIS NDVI time series clustering under dynamic time warping

Zhang, Zheng; Tang, Ping; Huo, Lianzhi; Zhou, Zeng-Guang

doi:10.1142/s0219691314610116

Cited by 21 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By combining time warping with distance measurements between elements of sequences, DTW is a nonlinear warping algorithm that decomposes a complex global optimization problem into local optimization problems using dynamic programming principle [23]. DTW has the advantage of time flexibility and was proven to achieve better results than the Euclidean distance measure for MODIS NDVI time series clustering [63]. Euclidean distance is a time-rigid method of comparing two sequences where each element of a sequence is compared with its associated element from the other sequence.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…DTW, on the other hand, is a time-flexible method that considers the temporal distortions of the time series ( Figure 6), which can be associated with amplitude scaling or translation, time scaling or translation, shape changes, or noise in the data [64]. NDVI time series clustering [63]. Euclidean distance is a time-rigid method of comparing two sequences where each element of a sequence is compared with its associated element from the other sequence.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

et al. 2019

View full text Add to dashboard Cite

The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The experimental results demonstrated the effectiveness of the proposed approach for hierarchical clustering of time series data. For time series with cloud noise and time distortion, Zhang et al [ 22 ] proposed an effective time series clustering framework including similarity measure, prototype calculation, clustering algorithm and cloud noise handling. The core of this framework was DTW distance and its corresponding averaging method, DTW barycenter averaging (DBA).…”

Section: Literature Reviewmentioning

confidence: 99%

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Liu

Chen

et al. 2018

PLoS ONE

View full text Add to dashboard Cite

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

show abstract

“…Early cloud removal techniques are mainly based on single or small-scale images due to limited remote sensing data sources and limited cloud detection accuracy. With the development of remote sensing technology, especially the free-open policy of the Landsat data, a long data record spanning more than four decades are presented to all researchers over the world to monitor the Earth [9], [10]. However, cloud and cloud shadow hinder further processing of Landsat time…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Landsat Data Automatic Cloud Removal Using Poisson Blending

Huo

Zhang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Cloud and cloud shadow are common issues in optical satellite imagery, which greatly reduce the usage of data archive. As for the Landsat data, great advances have been made on detecting cloud and cloud shadow. However, few studies were performed on Landsat cloud removal for large areas. To facilitate land cover dynamics studies with high temporal resolution, we present an automatic cloud removal algorithm in this paper. Specifically, For Landsat Collection 1 Level-1 surface reflectance products, the algorithm first builds a cloud mask from the Quality Assessment (QA) band, and then reconstructs cloud-contaminated portions based on multi-temporal Landsat images with temporal similarity. To further eliminate radiation differences between cloud-free and reconstructed regions, a Poisson blending algorithm is adopted. Besides, the efficiency of gradient-domain compositing is accelerated by the quad-tree approach. Experiments have been performed to process more than 50,000 Landsat 8 Operational Land Imager (OLI) images covering China from 2013 to 2017, which yield promising results in terms of radiometric accuracy and consistency for experimental images with cloud coverage less than 80%. The produced Landsat time series images with cloud removal can be further used for analyzing land cover and land change dynamics in China, and the proposed algorithm should be easily employed to produce cloud-free Landsat time series for other areas.

show abstract

MODIS NDVI time series clustering under dynamic time warping

Cited by 21 publications

References 11 publications

Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Multi-Temporal Landsat Data Automatic Cloud Removal Using Poisson Blending

Contact Info

Product

Resources

About