Article: Satellite image classification using deep learning model-ResNet Journal: International Journal of Data Mining, Modelling and Management (IJDMMM) 2026 Vol.18 No.1 pp.15 - 33 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). Inderscience Publishers - linking academia, business and industry through research

Title: Satellite image classification using deep learning model-ResNet

Authors: Pranali Kosamkar; Vrushali Kulkarni; Abdulrahim Shaikh; Geetika Agarwal; Inderjeet Balotia

Addresses: Dr. Vishwanath Karad MIT World Peace University, S.No.124, Paud Road Kothrud, Pune-411038, India ' Dr. Vishwanath Karad MIT World Peace University, S.No.124, Paud Road Kothrud, Pune-411038, India ' Dr. Vishwanath Karad MIT World Peace University, S.No.124, Paud Road Kothrud, Pune-411038, India ' Dr. Vishwanath Karad MIT World Peace University, S.No.124, Paud Road Kothrud, Pune-411038, India ' Dr. Vishwanath Karad MIT World Peace University, S.No.124, Paud Road Kothrud, Pune-411038, India

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).

Keywords: deep learning; ResNet; data mining; DM; artificial intelligence; AI; machine learning; ML; satellite image; remote sensing.

DOI: 10.1504/IJDMMM.2026.151838

International Journal of Data Mining, Modelling and Management, 2026 Vol.18 No.1, pp.15 - 33

Received: 07 Sep 2023
Accepted: 19 Nov 2024
Published online: 23 Feb 2026 *

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Title: Satellite image classification using deep learning model-ResNet

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