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Information Journal Paper

Title

A new object based feature extraction method using segmentation for classification of hyperspectral images.

Pages

  109-127

Abstract

 Hyper Spectral Images (HSI) collect a lot of information in hundreds of narrow spectral bands. This type of image has been more useful for a wide range of applications in ground surface identification. Here, there are some processes to achieve that proper information. So, finding a way to gain the best accuracy for collecting data has become an interesting field for scientists. As a result, in this paper, we introduced object-based Feature Extraction algorithms (FE) to find out such useful information. The proposed algorithm has four fundamental phases. In the first stage, we use an unsupervised FE such as the PCA algorithm to extract the most significant features of the image. Then, the Gabor filter would add to obtain the local features. In the third step, we use the K-means algorithm to make a segmentation map of the image. Finally, in the last stage, by considering the coordination between pixels of each region and the effects of local relations among neighbor pixels relating to the same object in the image by an appropriate transformation, a function introduced. As a consequence of all these stages, some important and efficient features of the proposed data would extract.

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  • Cite

    APA: Copy

    TABATABAEI, ZAHRA, & SHAHDOOSTI, HAMID REZA. (2020). A new object based feature extraction method using segmentation for classification of hyperspectral images.. ELECTRONIC INDUSTRIES, 11(2 ), 109-127. SID. https://sid.ir/paper/964248/en

    Vancouver: Copy

    TABATABAEI ZAHRA, SHAHDOOSTI HAMID REZA. A new object based feature extraction method using segmentation for classification of hyperspectral images.. ELECTRONIC INDUSTRIES[Internet]. 2020;11(2 ):109-127. Available from: https://sid.ir/paper/964248/en

    IEEE: Copy

    ZAHRA TABATABAEI, and HAMID REZA SHAHDOOSTI, “A new object based feature extraction method using segmentation for classification of hyperspectral images.,” ELECTRONIC INDUSTRIES, vol. 11, no. 2 , pp. 109–127, 2020, [Online]. Available: https://sid.ir/paper/964248/en

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