Crop classification at subfield level using RapidEye time series and graph theory algorithms

Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.

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The effect of EVI time series density on crop classification accuracy

Optik ◽

10.1016/j.ijleo.2017.11.157 ◽

2018 ◽

Vol 157 ◽

pp. 1065-1072 ◽

Cited By ~ 4

Author(s):

Yulin Zhan ◽

Shakir Muhammad ◽

Pengyu Hao ◽

Zheng Niu

Keyword(s):

Time Series ◽

Classification Accuracy ◽

Crop Classification

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DETECTING SERIES PERIODICITY WITH HORIZONTAL VISIBILITY GRAPHS

International Journal of Bifurcation and Chaos ◽

10.1142/s021812741250160x ◽

2012 ◽

Vol 22 (07) ◽

pp. 1250160 ◽

Cited By ~ 26

Author(s):

ANGEL NUÑEZ ◽

LUCAS LACASA ◽

EUSEBIO VALERO ◽

JOSE PATRICIO GÓMEZ ◽

BARTOLO LUQUE

Keyword(s):

Time Series ◽

Graph Theory ◽

Noise Reduction ◽

Time Domain ◽

Generic Properties ◽

Horizontal Visibility ◽

Visibility Graphs ◽

Visibility Algorithm ◽

In Series ◽

Periodic Signals

The horizontal visibility algorithm was recently introduced as a mapping between time series and networks. The challenge lies in characterizing the structure of time series (and the processes that generated those series) using the powerful tools of graph theory. Recent works have shown that the visibility graphs inherit several degrees of correlations from their associated series, and therefore such graph theoretical characterization is in principle possible. However, both the mathematical grounding of this promising theory and its applications are in its infancy. Following this line, here we address the question of detecting hidden periodicity in series polluted with a certain amount of noise. We first put forward some generic properties of horizontal visibility graphs which allow us to define a (graph theoretical) noise reduction filter. Accordingly, we evaluate its performance for the task of calculating the period of noisy periodic signals, and compare our results with standard time domain (autocorrelation) methods. Finally, potentials, limitations and applications are discussed.

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Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin

Agricultural Systems ◽

10.1016/j.agsy.2019.01.005 ◽

2019 ◽

Vol 171 ◽

pp. 36-50 ◽

Cited By ~ 12

Author(s):

Laura Piedelobo ◽

David Hernández-López ◽

Rocío Ballesteros ◽

Amal Chakhar ◽

Susana Del Pozo ◽

...

Keyword(s):

Time Series ◽

River Basin ◽

Landsat 8 ◽

Crop Classification ◽

Sentinel 2

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The Impact of the Oil Sector on Commodity Prices: Correlation or Causation?

Journal of Agricultural and Applied Economics ◽

10.1017/s1074070800003667 ◽

2010 ◽

Vol 42 (3) ◽

pp. 477-485 ◽

Cited By ~ 95

Author(s):

Sayed H. Saghaian

Keyword(s):

Time Series ◽

Graph Theory ◽

Causal Structure ◽

Commodity Prices ◽

Oil Prices ◽

Causal Link ◽

Energy Markets ◽

Oil Sector ◽

The Impact ◽

Causal Structures

The interconnections of agriculture and energy markets have increased through the rise in the new biofuel agribusinesses and the oil-ethanol-corn linkages. The question is whether these linkages have a causal structure by which oil prices affect commodity prices and through these links, instability is transferred from energy markets to already volatile agricultural markets. In this article, we present empirical results using contemporary time-series analysis and Granger causality supplemented by a directed graph theory modeling approach to identify the links and plausible contemporaneous causal structures among energy and commodity variables. The results show that although there is a strong correlation among oil and commodity prices, the evidence for a causal link from oil to commodity prices is mixed.

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