Link the remote sensing big data to the image features via wavelet transformation

Wang, Lizhe; Song, Wenzhe; Liu, Peng

doi:10.1007/s10586-016-0569-6

Cited by 62 publications

(15 citation statements)

References 26 publications

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“…1 There do not appear to be astroturfers among this group. However, review of other similar news and opinion sites such as the Guardian online edition, 2 the Sydney Morning Herald online edition, 3…”

Section: Discussionmentioning

confidence: 99%

“…This paper discusses the use of authorship attribution as a tool for detecting when one person writes under multiple noms de plume, sometimes intentionally giving the impression that numerous people are writing and, hence, creating a kind of “social proof” of the ideas that the person wants to promote. This is called “astroturfing” and appears in political and commercial contexts, perhaps aiming to influence voters for or against candidates in a political campaign (eg, during an election) or in product promotion . Detecting astroturfing is important because the astroturfer is engaging in deliberately deceptive behavior aimed at influencing the readers without disclosing their interests.…”

Section: Introductionmentioning

confidence: 99%

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Astroturfing detection in social media: a binary n‐gram–based approach

Peng

Detchon

Choo

et al. 2016

Concurrency and Computation

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Summary Astroturfing is appearing in numerous contexts in social media, with individuals posting product reviews or political commentary under a number of different names, and is of concern because of the intended deception. An astroturfer works with the aim of making it seem that a large number of people hold the same opinion, promoting a consensus based on the astroturfer's intentions. It is generally done for commercial or political advantage, often by paid writers or ideologically motivated writers. This paper brings the notion of authorship attribution to bear on the astroturfing problem, collecting quantities of data from public social media sites and analyzing the putative individual authors to see if they appear to be the same person. The analysis comprises a binary n‐gram method, which was previously shown to be effective at accurately identifying authors on a training set from the same authors, while this paper shows how authors on different social media turn out to be the same author. The method has identified numerous instances where multiple accounts are apparently being operated by a single individual.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Astroturfing detection in social media: a binary n‐gram–based approach

Peng

Detchon

Choo

et al. 2016

Concurrency and Computation

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“…The experimental results indicated the feasibility and the effectiveness of the proposed DTPM algorithm and DTC algorithm. Our future research direction includes (1) achieving the minimum dataset storage in the cloud ; (2) visualizing the mining results on Baidu Map; (3) implementing the distributed algorithms using other distributed platforms such as the G‐Hadoop platform ; (4) parallel modelling & simulation of crowd based on the mined trajectory pattern ; (5) exploring more trajectory sequence feature extraction methods for trajectory clustering ; and (6) exploring dictionary learning algorithms for taxi spatial data .…”

Section: Resultsmentioning

confidence: 99%

A cloud‐based taxi trace mining framework for smart city

Liu

et al. 2016

Softw Pract Exp

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As a well-known field of big data applications, smart city takes advantage of massive data analysis to achieve efficient management and sustainable development in the current worldwide urbanization process. An important problem in smart city is how to discover frequent trajectory sequence pattern and cluster trajectory. To solve this problem, this paper proposes a cloud-based taxi trajectory pattern mining and trajectory clustering framework for smart city. Our work mainly includes (1) preprocessing raw Global Positioning System trace by calling the Baidu API Geocoding; (2) proposing a distributed trajectory pattern mining (DTPM) algorithm based on SPARK; and (3) proposing a distributed trajectory clustering (DTC) algorithm based on SPARK. The proposed DTPM algorithm and DTC algorithm can overcome the high input/output overhead and communication overhead by adopting in-memory computation. In addition, the proposed DTPM algorithm can avoid generating redundant local trajectory patterns to significantly improve the overall performance. The proposed DTC algorithm can enhance the performance of trajectory similarity computation by transforming the trajectory similarity calculation into AND and OR operators. Experimental results indicate that DTPM algorithm and DTC algorithm can significantly improve the overall performance and scalability of trajectory pattern mining and trajectory clustering on massive taxi trace data.The remainder of this paper is organized as follows. Section 2 presents the related work. In Section 3, we present the problem definition. Sections 4 and 5 describe our proposed DTPM algorithm and trajectory cluster algorithm respectively. In Section 6, some experiments are demonstrated that show the scalability and efficiency of the proposed algorithms. Finally, Section 7 addresses the conclusion and future work. Figure 1. Overview of our framework.

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“…Both of these spatial consideration approaches protect most of the structural edges in the abundance maps. However, these methods basically preserve the conspicuous changes and efficiently remove low-level spatial stochastic noise, but they cannot separate the different levels of details and structures [43].…”

Section: Rolling Guidance Spatial Regularization Modelmentioning

confidence: 99%

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

et al. 2017

Self Cite

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Abstract:Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional abundance results. However, the traditional spatial sparse unmixing approaches can suppress discrete wrong unmixing points and smooth an abundance map with low-contrast changes, and it has no concept of scale difference. In this paper, to better extract the different levels of spatial details, rolling guidance based scale-aware spatial sparse unmixing (namely, Rolling Guidance Sparse Unmixing (RGSU)) is proposed to extract and recover the different levels of important structures and details in the hyperspectral remote sensing image unmixing procedure, as the different levels of structures and edges in remote sensing imagery have different meanings and importance. Differing from the existing spatial regularization based sparse unmixing approaches, the proposed method considers the different levels of edges by combining a Gaussian filter-like method to realize small-scale structure removal with a joint bilateral filtering process to account for the spatial domain and range domain correlations. The proposed method is based on rolling guidance spatial regularization in a traditional spatial regularization sparse unmixing framework, and it accomplishes scale-aware sparse unmixing. The experimental results obtained with both simulated and real hyperspectral images show that the proposed method achieves visual effects better and produces higher quantitative results (i.e., higher SRE values) when compared to the current state-of-the-art sparse unmixing algorithms, which illustrates the effectiveness of the rolling guidance based scale aware method. In the future work, adaptive scale-aware spatial sparse unmixing framework will be studied and developed to enhance the current idea.

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Link the remote sensing big data to the image features via wavelet transformation

Cited by 62 publications

References 26 publications

Astroturfing detection in social media: a binary n‐gram–based approach

Astroturfing detection in social media: a binary n‐gram–based approach

A cloud‐based taxi trace mining framework for smart city

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

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