Land-use classification via ensemble dropout information discriminative extreme learning machine based on deep convolution feature

Zhang, Tianle; Hou, Muzhou; Zhou, Tao; Zhao-de, Liu; Cheng, Wenjun; Cheng, Yangjin

doi:10.2298/csis191222010z

Cited by 3 publications

(2 citation statements)

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“…Table 3 shows big data analysis methods and IoT applications via machine learning methods such as classification, clustering, association rule, prediction, and time series [45][46][47]. Proposed ELM methods are used in maximum IoT applications such as social networking, bioinformatics, smart energy, smart home, e-government, and others compare to other machine learning methods [48][49][50][51][52]. ELM based framework on the cloud layer provides excellent performance at a high data rate.…”

Section: Proposed Framework Evaluationmentioning

confidence: 99%

Machine learning based distributed big data analysis framework for next generation web in IoT

Singh

Cha

Kim

et al. 2021

ComSIS

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For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.

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Section: Proposed Framework Evaluationmentioning

confidence: 99%

Machine learning based distributed big data analysis framework for next generation web in IoT

Singh

Cha

Kim

et al. 2021

ComSIS

View full text Add to dashboard Cite

show abstract

“…Various machine learning algorithms, especially support vector machine (SVM) [36], random forests (RF) [37], extreme gradient boosting (XGB) [38], logistic regression (LR) [39], and convolutional neural network (CNN) [40], are widely used in many research fields of geoscience. Remote sensing (RS) image classification is a common tool for land use survey, which has become more robust with the introduction of machine learning algorithms [41][42][43][44][45][46]. Supervised machine learning algorithms have obtained promising results in mineral prospectivity mapping [47][48][49][50], geo-hazard mapping and geo-risk assessment [51][52][53][54], biomass estimation [55][56][57][58], and dust source susceptibility mapping [59,60].…”

Section: Introductionmentioning

confidence: 99%

Suitability Assessment Method of Red Tourism Development Using Geospatial and Social Humanity Data: A Case Study of Ruijin City, East China

Qin

Cao

et al. 2023

Sustainability

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It is important to analyze the trend in land use changes and assess the suitability of resource development for protecting natural resources, developing ecological industries, and land use planning issues. Ruijin City is located in South Jiangxi and has abundant resources for red tourism development. By analyzing the landscape changes in land use and the spatial distribution characteristics of local red culture resources, a supervised machine learning-based prediction model was constructed to quantitatively assess the suitability of red tourism development in a geographic information system (GIS) and the R language environment using geographical, economical, and human-related datasets. The results revealed that: (i) the increasing of human activities and economic vitality provide a beneficial social environment for the development of tourism resources; (ii) highly concentrated red resources, or those with special significance, are conducive to developing red tourism resources; (iii) preferentially, central–eastern Ruijin was followed by the extension areas to peripheral towns, which are potentially suitable areas for the development of red scenic spots. Generally, the findings of this study were consistent with the conventional cognitions and lessons on tourism development, and the constructed evaluation system is expected to be promoted to similar research.

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Adaptive multiscale sparse unmixing for hyperspectral remote sensing image

Xie

et al. 2023

COMSIS J

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Sparse unmixing of hyperspectral images aims to separate the endmembers and estimate the abundances of mixed pixels. This approach is the essential step for many applications involving hyperspectral images. The multi scale spatial sparse hyperspectral unmixing algorithm (MUA) could achieve higher accuracy than many state-of-the-art algorithms. The regularization parameters, whose combinations markedly influence the unmixing accuracy, are determined by manually searching in the broad parameter space, leading to time consuming. To settle this issue, the adaptive multi-scale spatial sparse hyperspectral unmixing algorithm (AMUA) is proposed. Firstly, the MUA model is converted into a new version by using of a maximum a posteriori (MAP) system. Secondly, the theories indicating that andnorms are equivalent to Laplacian and multivariate Gaussian functions, respectively, are applied to explore the strong connections among the regularization parameters, estimated abundances and estimated noise variances. Finally, the connections are applied to update the regularization parameters adaptively in the optimization process of unmixing. Experimental results on both simulated data and real hyperspectral images show that the AMUA can substantially improve the unmixing efficiency at the cost of negligible accuracy. And a series of sensitive experiments were undertook to verify the robustness of the AMUA algorithm.

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Land-use classification via ensemble dropout information discriminative extreme learning machine based on deep convolution feature

Cited by 3 publications

References 0 publications

Machine learning based distributed big data analysis framework for next generation web in IoT

Machine learning based distributed big data analysis framework for next generation web in IoT

Suitability Assessment Method of Red Tourism Development Using Geospatial and Social Humanity Data: A Case Study of Ruijin City, East China

Adaptive multiscale sparse unmixing for hyperspectral remote sensing image

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