scholarly journals Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data

2018 ◽  
Vol 11 (1) ◽  
pp. 53 ◽  
Author(s):  
Lu Xu ◽  
Hong Zhang ◽  
Chao Wang ◽  
Bo Zhang ◽  
Meng Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Hyperspectral Image ◽  
Temporal Information ◽  
Series Data ◽  
Model Matching ◽  
Hyperspectral Image Processing ◽  
Temporal Models ◽  
Temporal Model ◽  
Crop Classification

With the increasing temporal resolution of space-borne SAR, large amounts of intensity data are now available for continues land observations. Previous researches proved the effectiveness of multitemporal SAR in land classification, but the characterizations of temporal information were still inadequate. In this paper, we proposed a crop classification scheme, which made full use of multitemporal SAR backscattering responses. In this method, the temporal intensity models were established by the K-means clustering method. The intensity vectors were treated as input features, and the mean intensity vectors of cluster centers were regarded as the temporal models. The temporal models summarized the backscatter evolutions of crops and were utilized as the criterion for crop discrimination. The spectral similarity value (SSV) measure was introduced from hyperspectral image processing for temporal model matching. The unlabeled pixel was assigned to the class to which the temporal model with the highest similarity belonged. Two sets of Sentinel-1 SAR time-series data were used to illustrate the effectiveness of the proposed method. The comparison between SSV and other measures demonstrated the superiority of SSV in temporal model matching. Compared with the decision tree (DT) and naive Bayes (NB) classifiers, the proposed method achieved the best overall accuracies in both VH and VV bands. For most crops, it either obtained the best accuracies or achieved comparable accuracies to the best ones, which illustrated the effectiveness of the proposed method.

scholarly journals J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Andrea Brunello ◽  
Enrico Marzano ◽  
Angelo Montanari ◽  
Guido Sciavicco
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Time Series Data ◽  
Temporal Information ◽  
Frequent Pattern ◽  
Series Data ◽  
The Novel ◽  
C4.5 Decision Tree ◽  
Business Setting ◽  
Interpretable Models

Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. This paper presents J48SS, a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The novel algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm is evaluated on a text classification task in a real business setting, as well as on a selection of public UCR time series datasets. Results show that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort.

scholarly journals A Stochastic Multivariate Irregularly Sampled Time Series Imputation Method for Electronic Health Records

2021 ◽  
Vol 1 (3) ◽  
pp. 166-181
Author(s):  
Muhammad Adib Uz Zaman ◽  
Dongping Du
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Electronic Health Records ◽  
Missing Values ◽  
Time Series Data ◽  
Temporal Information ◽  
Series Data ◽  
High Dimensional ◽  
Health Records ◽  
Electronic Health

Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do not consider the importance of temporal information embedded in EHR data. Besides, most time-dependent neural networks such as recurrent neural networks (RNNs) inherently consider the time steps to be equal, which in many cases, is not appropriate. This study presents a method using the gated recurrent unit (GRU), neural ordinary differential equations (ODEs), and Bayesian estimation to incorporate the temporal information and impute sporadically observed time series measurements in high-dimensional EHR data.

scholarly journals Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

2015 ◽  
Vol 7 (12) ◽  
pp. 16091-16107 ◽  
Author(s):  
Qingting Li ◽  
Cuizhen Wang ◽  
Bing Zhang ◽  
Linlin Lu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Object Based ◽  
Crop Classification

scholarly journals Automatic Crop Classification in Northeastern China by Improved Nonlinear Dimensionality Reduction for Satellite Image Time Series

2020 ◽  
Vol 12 (17) ◽  
pp. 2726 ◽  
Author(s):  
Yongguang Zhai ◽  
Nan Wang ◽  
Lifu Zhang ◽  
Lei Hao ◽  
Caihong Hao
Keyword(s):  
Time Series ◽  
Dimensionality Reduction ◽  
Time Series Data ◽  
Satellite Image ◽  
Series Data ◽  
Nonlinear Dimensionality Reduction ◽  
Distribution Map ◽  
Dimensionality Reduction Technique ◽  
Classification Tasks ◽  
Crop Classification

Accurate and timely information on the spatial distribution of crops is of great significance to precision agriculture and food security. Many cropland mapping methods using satellite image time series are based on expert knowledge to extract phenological features to identify crops. It is still a challenge to automatically obtain meaningful features from time-series data for crop classification. In this study, we developed an automated method based on satellite image time series to map the spatial distribution of three major crops including maize, rice, and soybean in northeastern China. The core method used is the nonlinear dimensionality reduction technique. However, the existing nonlinear dimensionality reduction technique cannot handle missing data, and it is not designed for subsequent classification tasks. Therefore, the nonlinear dimensionality reduction algorithm Landmark–Isometric feature mapping (L–ISOMAP) is improved. The advantage of the improved L–ISOMAP is that it does not need to reconstruct time series for missing data, and it can automatically obtain meaningful featured metrics for classification. The improved L–ISOMAP was applied to Landsat 8 full-band time-series data during the crop-growing season in the three northeastern provinces of China; then, the dimensionality reduction bands were inputted into a random forest classifier to complete a crop distribution map. The results show that the area of crops mapped is consistent with official statistics. The 2015 crop distribution map was evaluated through the collected reference dataset, and the overall classification accuracy and Kappa index were 83.68% and 0.7519, respectively. The geographical characteristics of major crops in three provinces in northeast China were analyzed. This study demonstrated that the improved L–ISOMAP method can be used to automatically extract features for crop classification. For future work, there is great potential for applying automatic mapping algorithms to other data or classification tasks.

scholarly journals COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 675-681 ◽  
Author(s):  
G. S. Phartiyal ◽  
D. Singh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Classification Task ◽  
Optical Data ◽  
Series Data ◽  
Study Region ◽  
Crop Classification

<p><strong>Abstract.</strong> Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a comparative analysis is made on the performances of various deep neural network (DNN) models for crop classification task using polarimetric synthetic aperture radar (PolSAR) and optical satellite data. For PolSAR data, Sentinel 1 dual pol SAR data is used. Sentinel 2 multispectral data is used as optical data. Five land cover classes including two crop classes of the season are taken. Time series data over the period of one crop cycle is used. Training and testing samples are measured and collected directly from the ground over the study region. Various convolutional neural network (CNN) and long short-term memory (LSTM) models are implemented, analysed, evaluated, and compared. Models are evaluated on the basis of classification accuracy and generalization performance.</p>

scholarly journals Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4522
Author(s):  
Yupeng Kang ◽  
Xinli Hu ◽  
Qingyan Meng ◽  
Youfeng Zou ◽  
Linlin Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Red Edge ◽  
Ndvi Time Series ◽  
Crop Classification

Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring. This is of great significance for guiding agricultural production and formulating agricultural policies. According to the characteristics of the GF-6 satellite’s newly-added red edge bands, wide field view and high-frequency imaging, the time series of vegetation indices about multi-temporal GF-6 WFV data are used for the study of land cover and crop classification. In this study, eight time steps of GF-6 WFV data were selected from March to October 2019 in Hengshui City. The normalized difference vegetation index (NDVI) time series and 10 different red edge spectral indices time series were constructed. Then, based on principal component analysis (PCA), using two feature selection and evaluation methods, stepwise discriminant analysis (SDA) and random forest (RF), the red edge vegetation index of normalized difference red edge (NDRE) was selected. Seven different lengths of NDVI, NDRE and NDVI&NDRE time series were reconstructed by the Savizky-Golay (S-G) smoothing algorithm. Finally, an RF classification algorithm was used to analyze the influence of time series length and red edge indices features on land cover and crop classification, and the planting structure and distribution of crops in the study area were obtained. The results show that: (1) Compared with the NDRE red edge time series, the NDVI time series is more conducive to the improvement of the overall classification accuracy of crops, and NDRE can assist NDVI in improving the crop classification accuracy; (2) With the shortening of NDVI and NDRE time series, the accuracy of crop classification is gradually decreased, and the decline is gradually accelerated; and (3) Through the combination of the NDVI and NDRE time series, the accuracy of crop classification with different time series lengths can be improved compared with the single NDVI time series, which is conducive to improving the classification accuracy and timeliness of crops. This study has fully tapped the application potential of the new red edge bands of GF-6 WFV time series data, which can provide references for crop identification and classification of time series data such as NDVI and red edge vegetation index of different lengths. At the same time, it promotes the application of optical satellite data with red edge bands in the field of agricultural remote sensing.

scholarly journals Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada

2021 ◽  
Vol 13 (7) ◽  
pp. 1394
Author(s):  
Qinghua Xie ◽  
Kunyu Lai ◽  
Jinfei Wang ◽  
Juan M. Lopez-Sanchez ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Growing Season ◽  
Optimal Combination ◽  
Series Data ◽  
Future Research ◽  
Sar Images ◽  
Crop Monitoring ◽  
Crop Classification

Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channelρ ρHHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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