Kernel-based extreme learning machine for remote-sensing image classification

Pal, Mahesh; Maxwell, Aaron E.; Warner, Timothy A.

doi:10.1080/2150704x.2013.805279

Cited by 178 publications

(76 citation statements)

References 24 publications

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“…Kernel based methods [22,23] solved the problem resulting from random distribution of hidden layer parameters in ELM and gain higher relevance to corresponding datasets as well as higher stability. Optimized by kernel function, semi-supervised extreme learning machine (SSELM) can achieve higher classification accuracy and robustness.…”

Section: Smooth Average Congestedmentioning

confidence: 99%

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

2017

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Abstract:There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition.

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Section: Smooth Average Congestedmentioning

confidence: 99%

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

2017

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“…Machine learning algorithms such as support vector machines and artificial neural networks have been widely used and tested many times in remote sensing from optical to radar data for image classification in past decades (Pal et al, 2013). Relatively newer classification algorithms such as extreme learning machine (ELM) (Pal, 2009), relevance vector machines (RVMs) (Demir and Erturk, 2007), incremental import vector machines (I 2 VM) (Roscher et al, 2012) and rotation-based SVM (RoSVM) (Xia et al, 2016) have been introduced into remote sensing community for data classification purposes and tested fewer times compared to common ones.…”

Section: Image Classificationmentioning

confidence: 99%

Balanced vs Imbalanced Training Data: Classifying Rapideye Data With Support Vector Machines

Üstüner¹,

Sanli²,

Abdikan³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4 KEY WORDS: Land cover classification, Imbalanced training data, Support Vector Machines, RapidEye, Agriculture ABSTRACT:The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

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“…In this way, a feature cube can be obtained, where the number of its feature maps is After L alternations of the joint spectral-spatial feature learning processes, SLN obtains spectral-spatial features that are then classified by the KELM classifier [15,17,65].…”

Section: Subspace Learning-based Networkmentioning

confidence: 99%

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Wei

Zhou

2017

Remote Sensing

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This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboring regions. SSR forms a deep architecture and is able to learn discriminative spectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single unified framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classification. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classification.

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Kernel-based extreme learning machine for remote-sensing image classification

Cited by 178 publications

References 24 publications

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

Balanced vs Imbalanced Training Data: Classifying Rapideye Data With Support Vector Machines

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

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