IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v18y2026i1p15-33.html

Satellite image classification using deep learning model-ResNet

Author

Listed:
  • Pranali Kosamkar
  • Vrushali Kulkarni
  • Abdulrahim Shaikh
  • Geetika Agarwal
  • Inderjeet Balotia

Abstract

Data mining framework and artificial intelligence (AI) have played a key part in all decision-making scenarios. Due to the significant expenses associated with creating training and testing datasets, we need to deal with a number of issues, object recognition, classification, and semantic segmentation in images of low spatial resolution. In this paper we first reviewed the machine learning and deep learning-based model for satellite health monitoring systems. We built the deep learning model - for satellite image classification. The dataset used is Satellite Image Classification Dataset-RSI-CB256. Two variants, ResNet-12 and ResNet-18 were tested on the dataset. The ResNet-18 showed over 0.94 accuracy for 5 number of epochs and the ResNet-12 showed 0.92 accuracy for training over 10 number of epochs. The result shows that the choice of employing the ResNet CNN architecture for Satellite Image Classification is certainly better than employing other available models such as FCNN, RCNN (with F-RCNN).

Suggested Citation

  • Pranali Kosamkar & Vrushali Kulkarni & Abdulrahim Shaikh & Geetika Agarwal & Inderjeet Balotia, 2026. "Satellite image classification using deep learning model-ResNet," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 18(1), pages 15-33.
    • Handle: RePEc:ids:ijdmmm:v:18:y:2026:i:1:p:15-33
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=151838
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijdmmm:v:18:y:2026:i:1:p:15-33. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.