Automated Remote Sensing Image Classification Method Based on FCM and SVM

Huang, Qirui; Wu, Guiying; Jian-ming, Chen; Chu, Hequn

doi:10.1109/rsete.2012.6260418

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…This strategy generally allows the reduction of the size of the training data required, but needs prior knowledge about the study site to construct the stratification [26]. In some ways, an unsupervised classification is used to provide data, e.g., [27], which proposed an approach that clustered the remote-sensing data by combining the fuzzy c-means clustering with SVM. This method does not have a specific view of target classes and works merely on the basis of clustering the number of classes; hence, it is not suitable for HSR images.…”

Section: Initial Estimationmentioning

confidence: 99%

Development of an Object-Based Interpretive System Based on Weighted Scoring Method in a Multi-Scale Manner

Kiani

Ebadi

Ahmadi

2019

IJGI

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For an accurate interpretation of high-resolution images, correct training samples are required, whose automatic production is an important step. However, the proper way to use them and the reduction of their defects should also be taken into consideration. To this end, in this study, the application of different combinations of training data in a layered structure provided different scores for each observation. For each observation (segment) in a layer, the scores corresponded to the obtained misclassification cost for all classes. Next, these scores were properly weighted by considering the stability of different layers, the adjacency analysis of each segment in a multi-scale manner and the main properties of the basic classes. Afterwards, by integrating the scores of all classes weighted in all layers, the final scores were produced. Finally, the labels were achieved in the form of collective wisdom, obtained from the weighted scores of all segments. In the present study, the aim was to develop a hybrid intelligent system that can exploit both expert knowledge and machine learning algorithms to improve the accuracy and efficiency of the object-based classification. To evaluate the efficiency of the proposed method, the results of this research were assessed and compared with those of other methods in the semi-urban domain. The experimental results indicated the reliability and efficiency of the proposed method.

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Section: Initial Estimationmentioning

confidence: 99%

Development of an Object-Based Interpretive System Based on Weighted Scoring Method in a Multi-Scale Manner

Kiani

Ebadi

Ahmadi

2019

IJGI

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“…SVM [14] is proposed by Corinna Cortes and Vapink in 1995. It shows many unique advantages in the small sample, nonlinear and high dimensional pattern recognition.…”

Section: Brief Introduction Of the Svm Principlementioning

confidence: 99%

The Target Classification of Optical Remote Sensing Image Based on Hierarchical Features and AdaBoost Algorithm

Ji¹,

Qin²,

Wang³

2017

IJSIP

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“…However, the classification hyperspectral data with traditional method will result in low classification precision and data redundancy because of great redundancy information in hyperspectral data. To deal with these difficulties, some supervised classification methods and unsupervised classification methods have been widely used [4][5][6][7]. Supervised classification methods, such as the neural networks [4], SVM [5,6], minimum distance classifier [7], multinomial logistic regression (MLR) [8], Maximum-likelihood or Bayesian estimation methods [9], can deal effectively with the hyperspectral data classification.…”

Section: Introductionmentioning

confidence: 99%

“…To deal with these difficulties, some supervised classification methods and unsupervised classification methods have been widely used [4][5][6][7]. Supervised classification methods, such as the neural networks [4], SVM [5,6], minimum distance classifier [7], multinomial logistic regression (MLR) [8], Maximum-likelihood or Bayesian estimation methods [9], can deal effectively with the hyperspectral data classification. Among these methods, machine learning methods, such as SVM and RVM [10][11][12], are usually superior to the others and have been successfully applied to hyperspectral data classification.…”

Section: Introductionmentioning

confidence: 99%

Relevance Vector Machine Classification of Hyperspectral Data Based on Principal Component Analysis and Linear Discriminant Analysis

Bao¹,

Li²,

Ren³

2017

dtcse

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Abstract. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic. Compared to support vector machines (SVM), the Bayesian formulation of the RVM avoids the set of free parameters of the SVM. However, the classification accuracy of RVM is not high when apply to hyperspectral data. A novel classification method based on RVM is presented in this paper. The method combine principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce the dimensionality of hyperspectral data. Firstly, PCA is used to the first dimension reduction and obtain nonsingular intra-class scatter matrix. Secondly, LDA is applied to the second dimension reduction and reduce the amounts of computation great. Finally, the relevance vector machine model is applied to remote sensing image classification. The hyperspectral data of 1992 Indian Pines has been used in this paper. The experimental results show that the proposed method not only improves overall accuracy than RVM and PCA-RVM, but also extend the ratio between inter-class distances and intra-class distances.

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Automated Remote Sensing Image Classification Method Based on FCM and SVM

Cited by 6 publications

References 8 publications

Development of an Object-Based Interpretive System Based on Weighted Scoring Method in a Multi-Scale Manner

Development of an Object-Based Interpretive System Based on Weighted Scoring Method in a Multi-Scale Manner

The Target Classification of Optical Remote Sensing Image Based on Hierarchical Features and AdaBoost Algorithm

Relevance Vector Machine Classification of Hyperspectral Data Based on Principal Component Analysis and Linear Discriminant Analysis

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