Hyperspectral Image Classification Using Relevance Vector Machines

Demir, Begüm; Ertürk, Sarp

doi:10.1109/lgrs.2007.903069

Cited by 174 publications

(76 citation statements)

References 23 publications

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“…The proposed method system architecture Classification process is also needed a special attention. K nearest neighbors (KNN) (Duda, et al, 2001), support vector machine (SVM) (Melgani & Bruzzone, 2004), (Mountrakis, et al, 2011), relevance vector machine (RVM) (Demir & Erturk, 2007) or any other classification method can be used for classification. The important point in use of hyperspectral data reduced by CGLDA is that classification method should take into consideration global and local pattern variations.…”

Section: Proposed Methodsmentioning

confidence: 99%

Hyperspectral dimension reduction using global and local information based linear discriminant analysis

Sakarya

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages of the usage of not only global pattern information, but also local pattern information are examined in hyperspectral image processing. In addition, not only tuning the parameters, but also an experimental analysis of the distribution of the hyperspectral data is demonstrated. Therefore, how global or local pattern variations play an important role in classification is examined. According to the experimental outcomes, the promising results are obtained for classification on hyperspectral images.

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Section: Proposed Methodsmentioning

confidence: 99%

Hyperspectral dimension reduction using global and local information based linear discriminant analysis

Sakarya

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…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. Rather than classification method by itself, input imagery and training set have more significance for obtaining high accuracy as each method is based on supervised learning (Kavzoglu, 2009).…”

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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“…Apart from feature extraction, designing an effective classifier is also an important way to promote the classification accuracy. For example, Support Vector Machine (SVM) and Relevance Vector Machine (RVM) have been successfully used for HSI classification [13,14]. Recently, Kernel-based Extreme Learning Machine HSIs and to reduce intraclass variations.…”

Section: Introductionmentioning

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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Hyperspectral Image Classification Using Relevance Vector Machines

Cited by 174 publications

References 23 publications

Hyperspectral dimension reduction using global and local information based linear discriminant analysis

Hyperspectral dimension reduction using global and local information based linear discriminant analysis

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

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

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