Interpolation and context magnification framework for classification of scene images

Anas Tukur Balarabe, Ivan Jordanov

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recently, there has been an upsurge in publicly available remote sensing image classification datasets. Standard CNNs and pre-trained architectures have been applied for scene classification tasks. However, transfer learning models accept specific image dimensions as the minimum required size for their respective image input layers. Depending on the size of the input image, the final feature map might not contain the discriminative information needed for accurately classifying the dataset categories. The proposed technique effectively enables and enhances a transfer learning model (Xception) to be applied to scene classification tasks. The model works on an adaptive framework that interpolates images and selects an appropriate dilation layer to enhance the quality of extracted features for improved classification. This approach is evaluated on the EuroSAT, a dataset with images of 64x64 pixels, UCM and AID datasets, respectively. We recorded 98.55%, 99.22%, and 96.15% accuracy for the EuroSAT, UCM, and AID datasets, respectively. Our model and the reported results have opened the potential of the Xception, which in our view, has not been given its fair share of attention, despite its efficient parameter utilisation.

Original languageEnglish
Title of host publicationProceedings of the 16th Multi Conference on Computer Science and Information Systems: MCCSIS 2022
EditorsYingcai Xiao, Ajith Abraham, Guo Chao Peng, Jörg Roth
PublisherIADIS Press
Number of pages8
ISBN (Electronic)9789898704429
Publication statusPublished - 22 Jul 2022
Event16th Multi Conference on Computer Science and Information Systems: MCCSIS 2022 - Lisbon, Portugal
Duration: 19 Jul 202222 Jul 2022


Conference16th Multi Conference on Computer Science and Information Systems


  • Deep Learning
  • Image Interpolation
  • Scene classification
  • Transfer Learning

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