Microwave remote sensing is an important substitute for rice growth monitoring in cloudy area. Fields identification is the most basic task for tracking rice crop cultivation. A series of sophisticated classification methods such as machine learning techniques have been applied to mapping crop planting area. However, these methods are weak in interpreting the physical mechanism of identifying crop fields, and hardly illustrate the backscattering difference between rice fields and other ground objects. Besides, enormous training samples and computational expense are demanded to achieve high recognition accuracy, which limited their application to rapid and large-area agricultural monitoring. In this study, C-band quad-pol backscattering coefficients of different land cover types were extracted from RADARSAT-2 PolSAR (Polarimetric Synthetic Aperture Radar) data during the reproductive period of rice fields. The backscatter statistical characteristics of rice fields and other ground types in different polarimetric bands were visualized and analyzed using boxplots. Based on the scatter performance of the five interested ground objects, a simple decision tree scheme was proposed to identify rice paddy fields. The overall accuracy is 88.65% and Kappa coefficient is 0.77. Meanwhile, a land cover classification map of the study area was obtained though the hierarchical judgement of decision tree strategy.

Recent satellite missions have provided a variety of high spatial resolution, multi-spectral, and high-frequency revisit remote sensing datasets. The collaborative use of optical and synthetic aperture radar (SAR) imagery in remote sensing applications attracts considerable attention. The purpose of this paper is to investigate the contribution of both data to the rice planting area mapping. In particular, the red-edge band was introduced to construct a red-edge vegetation index based on Sentinel-2 data. C-band quad-pol Radarsat-2 data was also used. We finally used the random forest algorithm, collaborating with optical and radar data to map rice planting area. We found that the red-edge band and red-edge vegetation index can improve the classification accuracy compared to the classifier using NIR (near-infrared) band and NDVI (Normalized Difference Vegetation Index). The result shows that the jointly use of optical and radar data is feasible to map rice planting area. The overall accuracy, recall and F-measure are 0.9441, 0.9598 and 0.9680, respectively. Index Terms - red edge, SAR, rice mapping, random forest, Sentinel-2, Radarsat-2

With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such data offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume pose great challenges on efficient analysis of economic big data. The existing methods have primarily analyzed economic data from the perspective of econometrics, which involves limited indicators and demands prior knowledge of economists. When embracing large varieties of economic factors, these methods tend to yield unsatisfactory performance. To address the challenges, this paper presents a new framework for efficient analysis of high-dimensional economic big data based on innovative distributed feature selection. Specifically, the framework combines the methods of economic feature selection and econometric model construction to reveal the hidden patterns for economic development. The functionality rests on three pillars: (i) novel data pre-processing techniques to prepare high-quality economic data, (ii) an innovative distributed feature identification solution to locate important and representative economic indicators from multidimensional data sets, and (iii) new econometric models to capture the hidden patterns for economic development. The experimental results on the economic data collected in Dalian, China, demonstrate that our proposed framework and methods have superior performance in analyzing enormous economic data.

Summary The continuous growth of resource description framework (RDF) data poses an important challenge on RDF data partitioning that is a vital technique for effective cloud storage. Recently, many partitioning algorithms for large RDF data have been developed, and most of them are based on graph partitioning. However, existing graph partitioning methods could not partition asymmetric RDF data effectively, resulting in a lower performance for cloud storage. This paper proposes a balanced RDF graph partitioning algorithm for storing massive RDF data on cloud. We first devise a modularity‐based multi‐level label propagation algorithm (MMLP) to partition RDF graph roughly and then use a balanced K‐mediods clustering algorithm for finalk‐way partitioning. Balanced RDF graph partitioning algorithm designs an effective label update rule and a balanced modification strategy to achieve a high quality coarsening result and make the partition as equilibrium as possible. Experiments are carried on two representative RDF benchmarks and one real RDF dataset by comparison with two representative graph partitioning methods, that is, METIS and MLP+METIS. Results demonstrate that our proposed scheme can produce a high‐quality partition for massive RDF data storage on cloud. Copyright © 2016 John Wiley & Sons, Ltd.

Mobile sensor networks are desirable in a variety of application scenarios, in which information collection is no doubt of great importance. In this paper, we present a mobile sensor network architecture consisting of a potentially large number of mobile sensors and a single or multiple stationary s

Congestion in wireless sensor networks not only causes packet loss, but also leads to excessive energy consumption. Therefore congestion in WSNs needs to be controlled in order to prolong system lifetime. In addition, this is also necessary to improve fairness and provide better quality of service (QoS), which is required by multimedia applications in wireless multimedia sensor networks. In this paper, we propose a novel upstream congestion control protocol for WSNs, called priority-based congestion control protocol (PCCP). Unlike existing work, PCCP innovatively measures congestion degree as the ratio of packet inter-arrival time along over packet service time. PCCP still introduced node priority index to reflect the importance of each sensor node. Based on the introduced congestion degree and node priority index, PCCP utilizes a cross-layer optimization and imposes a hop-by-hop approach to control congestion. We have demonstrated that PCCP achieves efficient congestion control and flexible weighted fairness for both single-path and multi-path routing, as a result this leads to higher energy efficiency and better QoS in terms of both packet loss rate and delay.