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A Novel Method for Retrieval of Remote Sensing Image Using Wavelet Transform and HOG

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 940))

Abstract

This paper presents the novel approach for retrieving remote sensing images which are based on low level feature extraction like color, texture, and shape. The HSV, Color Correlogram and Color Moments have been used for color feature extraction. Then image has decomposed image using wavelet transform to extract texture feature. Finally, shape feature is extracted using Histogram of oriented gradients (HOG). HOG feature is very useful for finding the local object appearance and shape. KNN classifier is used for training the image features and retrieving similar images. In this paper, UC Merced Land use Land Cover remote sensing Dataset has been used for the experiment. The result of the experiment shows that the combination of wavelet and HOG gives better accuracy and retrieval performance than discrete wavelet transform DWT.

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Author information

Authors and Affiliations

  1. MIT Academy of Engineering, Pune, India

    Minakshi N. Vharkte

  2. Jawaharlal Nehru Engineering College, Aurangabad, India

    Vijaya B. Musande

Authors
  1. Minakshi N. Vharkte
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  2. Vijaya B. Musande
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Corresponding author

Correspondence to Minakshi N. Vharkte .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Vharkte, M.N., Musande, V.B. (2020). A Novel Method for Retrieval of Remote Sensing Image Using Wavelet Transform and HOG. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_50

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