Recognition and Extraction of RS images from Remote Sensing Archives using Content Based Texture Features

Authors

  • Suganya

Keywords:

Remote sensing, Texture descriptors, Morphological operators, content based image retrieval (CBIR).

Abstract

Retrieval of remote sensing image from the large database is the difficult task. To solve this problem in this paper we are presenting the multiscale descriptors like circular covariance histogram, rotation invariant point tripletsare used which are the  effective texture descriptors. To overcome some of the limitations of the circular covariance histogram and rotation invariant point triplet, we introduce new descriptors Fourier power spectrum and quasi flatzone which consider the important texture properties like coarseness and directionality in the image which is the important property of the remote sensing images. The proposed system achieves best retrieval performance and alsoreduces the length of the feature vector. UC Merced land use land cover data set is used to evaluate the effectiveness of the descriptors which is the recently introduced dataset.

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Published

27-04-2014

How to Cite

S.Suganya. (2014). Recognition and Extraction of RS images from Remote Sensing Archives using Content Based Texture Features. ERES International Journal of Bio Signal and Image Processing, 1(1), 9–13. Retrieved from https://eresjournals.org/journals/index.php/ijbsip/article/view/44