Remote Sensing Image Classification Based on Ensemble Extreme Learning Machine With Stacked Autoencoder

Lv, Fei; Han, Min; Qiu, Tie

doi:10.1109/access.2017.2706363

Cited by 75 publications

(28 citation statements)

References 23 publications

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“…For instance, machine learning examples include bagging and boosting, which guard against overtraining or combine multiple weak learners into a strong learner (e.g., [22]); the no-free-lunch theorem is also an expression of underlying model selection ambiguities [23]. Ensemble learning continues to be an active field of AI research (e.g., [24]- [28]). In statistics, multi-model inference involves addressing model selection problems with linear combinations of different but similarly-performing linear models, sometimes weighting constituent models using information theoretic or Bayesian criteria [29], [30].…”

Section: Multi-methods Ensemble: Conceptmentioning

confidence: 99%

A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West

Fleming¹,

Goodbody

2019

IEEE Access

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Hydroelectric power generation, water supplies for municipal, agricultural, manufacturing, and service industry uses including technology-sector requirements, dam safety, flood control, recreational uses, and ecological and legal constraints, all place simultaneous, competing demands on the heavily stressed water management infrastructure of the mostly arid American West. Optimally managing these resources depends on predicting water availability. We built a probabilistic nonlinear regression water supply forecast (WSF) technique for the US Department of Agriculture, which runs the largest stand-alone WSF system in the US West. Design criteria included improved accuracy over the existing system; uncertainty estimates that seamlessly handle complex (heteroscedastic, non-Gaussian) prediction errors; integration of physical hydrometeorological process knowledge and domain-specific expert experience; ability to accommodate nonlinearity, model selection uncertainty and equifinality, and predictor multicollinearity and high dimensionality; and relatively easy, low-cost implementation. Some methods satisfied some of these requirements but none met all, leading us to develop a novel, interdisciplinary, and pragmatic prediction metasystem through a carefully considered synthesis of well-established, off-the-shelf components and approaches, spanning supervised and unsupervised machine learning, nonparametric statistical modeling, ensemble learning, and evolutionary optimization, focusing on maintaining but radically updating the principal components regression framework widely used for WSF. Testing this integrated multi-method prediction engine demonstrated its value for river forecasting; USDA adoption is a landmark for transitioning machine learning from research into practice in this field. Its ability to handle all the foregoing design criteria and requirements, which are not unique to WSF, suggests potential for extension to complex probabilistic prediction problems in other fields.

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Section: Multi-methods Ensemble: Conceptmentioning

confidence: 99%

A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West

Fleming¹,

Goodbody

2019

IEEE Access

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“…Furthermore, other features such as SIFT and SURF [13], and subspace extraction methods such as PCA and ICA [14], are also used in this application. These features can be further integrated with classifiers such as SVM, boosting, and neural networks to archive full classifier training [15,16]. Recently, a framework named deep learning has been increasingly used for object detection or classification by researchers.…”

Section: Related Workmentioning

confidence: 99%

A Vehicle Recognition Algorithm Based on Deep Transfer Learning with a Multiple Feature Subspace Distribution

Wang

Cai

et al. 2018

Sensors

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Vehicle detection is a key component of environmental sensing systems for Intelligent Vehicles (IVs). The traditional shallow model and offline learning-based vehicle detection method are not able to satisfy the real-world challenges of environmental complexity and scene dynamics. Focusing on these problems, this work proposes a vehicle detection algorithm based on a multiple feature subspace distribution deep model with online transfer learning. Based on the multiple feature subspace distribution hypothesis, a deep model is established in which multiple Restricted Boltzmann Machines (RBMs) construct the lower layers and a Deep Belief Network (DBN) composes the superstructure. For this deep model, an unsupervised feature extraction method is applied, which is based on sparse constraints. Then, a transfer learning method with online sample generation is proposed based on the deep model. Finally, the entire classifier is retrained online with supervised learning. The experiment is actuated using the KITTI road image datasets. The performance of the proposed method is compared with many state-of-the-art methods and it is demonstrated that the proposed deep transfer learning-based algorithm outperformed existing state-of-the-art methods.

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“…Weng et al [ 34 ] proposed a classification method based on deep learning, which combines convolutional neural networks and ELM to improve classification performance. Han et al [ 35 ] proposed a remote sensing image classification algorithm using stacked autoencoder and ensemble of ELM named SAE-ELM.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Han

2018

Sensors

Self Cite

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Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classifier to improve the classification efficiency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classification. Then, Q-statistic is used to select base classifiers. Finally, the results are obtained by using the voting method. Three simulation examples, classification of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.

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Remote Sensing Image Classification Based on Ensemble Extreme Learning Machine With Stacked Autoencoder

Cited by 75 publications

References 23 publications

A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West

A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West

A Vehicle Recognition Algorithm Based on Deep Transfer Learning with a Multiple Feature Subspace Distribution

Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

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