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5th International Conference on e-Learning (econf)

作者： El Rahman, Sahar A. Benha Univ Fac Engn Shoubra Elect Comp Syst & Commun Elect Dept Cairo Egypt

ISBN: (纸本)9781467394314

Hyperspectral imaging is employed in a broad array of applications. The usual idea in all of these applications is the requirement for classification of a hyperspectral image data. Where Hyperspectral data consists of many bands - up to hundreds of bands - that cover the electromagnetic spectrum. This results in a hyperspectral data cube that contains approximately hundreds of bands - which means BIG DATA CHALLENGE. In this paper, unsupervised hyperspectral image classification algorithm, in particular, Iterative Self-Organizing Data Analysis Technique algorithm (isodata) algorithm used to produce a classified image and extract agricultural information, using ENVI (Environment of Visualizing Images) that is a software application utilized to process and analyze geospatial imagery. The study area, which has been applied on is Florida, USA. Hyperspectral dataset of Florida was generated by the SAMSON sensor. In this paper, the performance was evaluated on the base of the accuracy assessment of the process after applying Principle Component Analysis (PCA) and isodata algorithm. The overall accuracy of the classification process is 75.6187%.

关键词： Hyperspectral Imaging isodata algorithm Image Classificatio Unsupervised Classification

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An Improved isodata algorithm for Hyperspectral Image Classification 7

An Improved ISODATA Algorithm for Hyperspectral Image Classi...

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7th International Congress on Image and Signal Processing (CISP)

作者： Wang, Qian Li, Qingli Liu, Hongying Wang, Yiting Zhu, Jianzhong E China Normal Univ Sch Informat Sci & Technol Shanghai 200241 Peoples R China E China Normal Univ Inst Adv Interdisciplinary Res Shanghai 200062 Peoples R China

ISBN: (纸本)9781479958351

Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques algorithm (isodata) clustering algorithm which is an unsupervised classification algorithm is considered as an effective measure in the area of processing hyperspectral images. In this paper, an improved isodata algorithm is proposed for hyperspectral images classification. The algorithm takes the maximum and minimum spectrum of the image into consideration and determines the initial cluster center by the stepped construction of spectrum accurately. The classification experiment results show that using the improved isodata algorithm can determine the initial cluster number adaptively. In comparison with the SAM (Spectral Angle Mapper) algorithm and the original isodata algorithm, a better performance of the proposed isodata method is shown in the part of results.

关键词： isodata algorithm hyperspectral clustering classification

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An Improved Fuzzy isodata algorithm for Credit Risk Assessment of the EIT Enterprises

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Modern Economy 2012年 3卷 686-689页

作者： Zongfang Zhou School of Management and Economics University of Electronic Science and Technology of China Chengdu China

We proposed an improved fuzzy isodata algorithm for credit risk assessment of the emerging information technol-ogy enterprise in this paper. Firstly, as the uncertainty of the EIT enterprise is relatively large, we set a reference sample and an initial clustering center matrix so that we overcame the shortcomings of traditional isodata algorithm and improved the reliability of fuzzy clustering analysis. Secondly, we proposed the steps of evaluating the EIT enterprises’ credit risk with improved fuzzy isodata algorithm. Last but not least, we assessed 10 EIT enterprises’ credit risk of a certain city, which proved the effectiveness and operability.

关键词： EIT Enterprise Credit Risk Reference Sample isodata algorithm Fuzzy Clustering

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A Hybrid PSO-isodata algorithm for remote sensing image segmentation

A Hybrid PSO-ISODATA Algorithm for remote sensing image segm...

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International Conference on Industrial Control and Electronics Engineering (ICICEE)

作者： Ma Cai-hong Dai Qin Liu Shi-Bin Chinese Acad Sci Ctr Earth Observat & Digital Earth Grad Univ Beijing Peoples R China Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100864 Peoples R China

ISBN: (纸本)9780769547923

Remote sensing image segmentation is a very important process in the remote sensing information extraction area. In order to avoid disadvantages of the current algorithms, this paper proposed a new algorithm combined PSO and isodata. The hybrid PSO-isodata algorithm first changes the color space of the images. Then, the initial cluster number is determined by the combined algorithm, and finally, the automatic segmentation of remote sensing images is achieved through multiple iterations. Many segmentation experiments on different spatial resolution remote sensing images using the proposed methods in this paper, we also compared the method to the current existing methods such as K-means, PSO, isodata, and PSO-K-means methods. The results show that the hybrid PSO-isodata algorithm can determine the initial cluster number adaptively, avoid the local optima of K-means and isodata algorithms, increase the searching capability of PSO, and the segmentation results are much more close to the actual situation.

关键词： PSO algorithm isodata algorithm feature distance remote sensing image segmentation formatting

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An Improved isodata algorithm for Hyperspectral Image Classification

An Improved ISODATA Algorithm for Hyperspectral Image Classi...

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International Congress on Image and Signal Processing

作者： Qian Wang Qingli Li Hongying Liu Yiting Wang Jianzhong Zhu Sch. of Inf. Sci. & Technol. East China Normal Univ. Shanghai China

ISBN: (纸本)9781479958368

关键词： data analysis hyperspectral imaging image classification iterative methods pattern clustering remote sensing unsupervised learning SAM algorithm clustering algorithm hyperspectral image classification hyperspectral remote sensing information processing improved isodata algorithm iterative self-organizing data analysis techniques algorithm spectral angle mapper algorithm unsupervised classification algorithm algorithm design and analysis Classification algorithms Clustering algorithms Hyperspectral imaging Software algorithms isodata algorithm classification clustering hyperspectral

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A New isodata Image Segmentation algorithm Based on Intuitionistic Fuzzy

A New ISODATA Image Segmentation Algorithm Based on Intuitio...

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International Conference on Sport Material, Modelling and Simulation

作者： Wang, Yinlong Lin, Kecheng Yu, Hongmei Li, Qianjin Li, Zhixiang Wang, Xiwu Fifth Dept Ordnance Engn Coll Shijiazhuang Peoples R China Wuhan Ordnance NCO Acad PLA Wuhan Peoples R China

ISBN: (纸本)9783037850411

isodata algorithm is capable of splitting and merging categories automatically. However, this kind of hard clustering fails to take into consideration the characteristics of image itself and human visual features. So its effect is generally not as good as that of fuzzy clustering algorithm. For most fuzzy recognition methods, if they are to be applied, the number of categories must be set beforehand. Besides, there is inherent defect in traditional Fuzzy algorithms. By contrast, intuitionistic fuzzy is a kind of improvement to make up the deficiencies of traditional fuzzy theory. Based on the advantages of isodata algorithm and intuitionistic fuzzy, with those critical functions which are related to membership and non-membership functions used as the measurement for clustering, this thesis is to propose a kind of isodata algorithm that is based on intuitionistic fuzzy, and to introduce membership function that has been improved for practical purposes. This kind of function takes region as the sample to be classified. Finally, this thesis is to verify the effectiveness of the proposed algorithm by applying it to Image Segmentation.

关键词： Image Segmentation isodata algorithm Intuitionistic Fuzzy

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isodata classification with parameters estimated by evolutionary approach

ISODATA classification with parameters estimated by evolutio...

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8th International Conference on Intelligent Systems - Theories and Applications (SITA)

作者： Merzougui, M. Nasri, M. Bouali, B. Univ Mohammed 1 LABO MATSI ESTO BP 473 Oujda Morocco Univ Mohammed 1 FSO LABO AGA Oujda Morocco

ISBN: (纸本)9781479902996

The isodata algorithm is an unsupervised data classification algorithm. Its result depends strongly on two parameters: distance threshold for the union of clusters and threshold of typical deviation for the division of a cluster. A bad choice of these two parameters leads the algorithm to spiral out of control leaving the end only one class. To determine these parameters and improvements to this algorithm, evolution strategies are used. An evolutionary algorithm is adapted to estimate the two optimal thresholds to be used by the algorithm then isodata. This approach is validated on simulation examples. The experimental results confirm the favorable convergence speed and good performance of the proposed algorithm.

关键词： Classification evolutionary strategies isodata algorithm

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Application of region-based segmentation and neural network edge detection to skin lesions

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2004年第1-2期28卷 61-68页

作者： Rajab, MI Woolfson, MS Morgan, SP Univ Nottingham Sch Elect & Elect Engn Nottingham NG7 2RD England

This paper proposes two approaches to the skin lesion image segmentation problem. The first is a mainly region-based segmentation method where an optimal threshold is determined iteratively by an isodata algorithm. The second method proposed is based on neural network edge detection and a rational Gaussian curve that fits an approximate closed elastic curve between the recognized neural network edge patterns. A quantitative comparison of the techniques is enabled by the use of synthetic lesions to which Gaussian noise is added. The proposed techniques are also compared with an established automatic skin segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties the iterative thresholding method provides the best performance over a range of signal to noise ratios. Iterative thresholding technique is also demonstrated to have similar performance when tested on real skin lesions. (C) 2003 Published by Elsevier Ltd.

关键词： segmentation isodata algorithm pattern recognition synthetic lesion skin lesion

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A systematic method based on statistical pattern recognition for estimating product quality on-line

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Journal of University of Science and Technology Beijing 2003年第1期10卷 69-73页

作者： Guang Li, Huade Li, Shaoyuan Sun, and Zhengguang XuInformation Engineering School, University of Science and Technology Beijing, Beijing 100083, China Information Engineering School University of Science and Technology Beijing Beijing 100083 China

To avoid the complexity of building mechanistic models by studying the inner nature of the object, a systematic method based on statistical pattern recognition is developed in order to estimate the product quality on-line. The mapping relationship between a feature space and a product quality space can be built by using regression analysis, and in applying clustering analysis the product quality space can be partitioned automatically. Eventually, estimating product quality on-line can be accomplished by sorting the mapped data in the partitioned quality space. A concrete problem is proposed which has a relatively small ratio of training data to input variables. By implementing the method mentioned above, a satisfying result has been achieved. Furthermore, the further question about choosing suitable mapping methods is briefly discussed.

关键词： pattern recognition regression analysis clustering analysis isodata algorithm sorting algorithm

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SKIN CANCER PARAMETERISATION algorithm BASED ON EPILUMINISCENCE IMAGE PROCESSING

SKIN CANCER PARAMETERISATION ALGORITHM BASED ON EPILUMINISCE...

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9th IEEE International Symposium on Signal Processing and Information Technology

作者： Garcia Zapirain, B. Mendez Zorrilla, A. Ruiz Oleagordia, I. Nunez, G. Abtane, A. Univ Deusto EVIDA PAS Grp Engn Fac ESIDE Bilbao 48007 Spain

ISBN: (纸本)9781424459490

In this paper a digital system for enhancing the diagnosis of skin cancer is presented. The algorithm is based on the standard ABCD dermatologic protocol, and on the measurements of contours and surfaces of some specific areas into the mole image captured by epiluminiscence techniques. Automatic algorithms of digital image processing have been developed in order to detect the appropriate segmentation of the images of suspected moles. This allows one to calculate some quantitative features based on the external contour using the isodata algorithm. In addition, other features are found based on the internal contours segmented using color heterogeneity criteria. These small internal moles help the diagnosis of the skin cancer to extract information about propagation and irregularities. The database used consists of 65 images already catalogued by dermatologists and the results are successful according to the assessment of medical experts.

关键词： Skin Cancer diagnosis system mole ABCD rules segmentation isodata algorithm color heterogeneity

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