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American Journal of Remote Sensing

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Volume 1, Issue 2, April 2013

  • Improving Satellite Image Segmentation Using Evolutionary Computation

    Mohamad M. Awad

    Issue: Volume 1, Issue 2, April 2013
    Pages: 13-20
    Received: 14 March 2013
    Accepted:
    Published: 2 April 2013
    DOI: 10.11648/j.ajrs.20130102.11
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    Abstract: Segmentation is the process of dividing an imageinto disjoint regions. It is the most important task in image processing where the success of the object recognition depends strongly on the efficiency of the segmentation process. The most popular and important segmentation methods are clustering such asFuzzy c-Means (FCM), Iterative Self-Organizing ... Show More
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  • An Efficient Hybrid Classification System for High Resolution Remote Sensor Data

    Roopesh Tamma, T. Ch. Malleswara Rao, G. Jaisankar

    Issue: Volume 1, Issue 2, April 2013
    Pages: 21-32
    Received: 15 March 2013
    Accepted:
    Published: 2 April 2013
    DOI: 10.11648/j.ajrs.20130102.12
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    Abstract: The classification of aerial and satellite remote sensing data has become a challenging problem due to the recent advances in remote sensor technology that led to higher spatial and spectral resolutions. This research paper presents novel sensor independent algorithms and techniques for dealing with the challenges of classification of high volume r... Show More
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  • Remote Mine Sensing Technology by Using IR Images

    Nobuhiro Shimoi, Yoshihiro Takita

    Issue: Volume 1, Issue 2, April 2013
    Pages: 33-37
    Received: 15 April 2013
    Accepted:
    Published: 2 April 2013
    DOI: 10.11648/j.ajrs.20130102.13
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    Abstract: This paper proposes an IR camera system that performs the task of removing mines for humanitarian purposes. Because of the high risks involved, it is necessary to conduct mine detection from the most remote endeavoring. By making use of infrared ray (IR) cameras, scattered mines can be detected from remote locations. In the case of mines buried in ... Show More
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  • A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering

    E. A. Zanaty, Ashraf Afifi

    Issue: Volume 1, Issue 2, April 2013
    Pages: 38-46
    Received: 14 April 2013
    Accepted:
    Published: 2 April 2013
    DOI: 10.11648/j.ajrs.20130102.14
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    Abstract: Many clustering and segmentation algorithms suffer from the limitation that the number of clusters/segments is specified manually by human operators. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. Thus, the estimation of optimal cluster number during th... Show More
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  • A visual mining based fame work for classification accuracy estimation

    Arun, Pattathal Vijayakumar

    Issue: Volume 1, Issue 2, April 2013
    Pages: 47-52
    Received: 12 April 2013
    Accepted:
    Published: 2 April 2013
    DOI: 10.11648/j.ajrs.20130102.15
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    Abstract: Classification techniques have been widely used in different remote sensing applications and correct classi-fication of mixed pixels is a tedious task. The problem is more complex with the classification of hyperspectral data and requires a thorough analysis. Traditional approaches adopt various statistical parameters, however does not facilitate ... Show More
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  • Improved Region Growing Method for Magnetic Resonance Images (MRIs) Segmentation

    E. A. Zanaty

    Issue: Volume 1, Issue 2, April 2013
    Pages: 53-60
    Received: 3 May 2013
    Accepted:
    Published: 30 May 2013
    DOI: 10.11648/j.ajrs.20130102.16
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    Abstract: Segmentation of magnetic resonance images (MRIs) is challenging due to the poor image contrast and artifacts that result in missing tissue boundaries, i.e. pixels inside the region and on the boundaries have similar intensity. In this paper, we adapt a region growing method to segment MRIs which contain weak boundaries between different tissues. Th... Show More
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