Deep learning earth observation classification using ImageNet pretrained networks

Dimitrios Marmanis, Mihai Datcu, Thomas Esch, Uwe Stilla

Research output: Contribution to journalArticlepeer-review

531 Scopus citations


Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entirely different classification problem, namely, the ImageNet challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised CNN classifier, along with their class labels, effectively training the system. Through this two-stage framework, we successfully deal with the limited-data problem in an end-to-end processing scheme. Comparative results over the UC Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1% up to 92.4%. Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach.

Original languageEnglish
Article number7342907
Pages (from-to)105-109
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number1
StatePublished - Jan 2016


  • Convolutional neural networks (CNNs)
  • Deep learning (DL)
  • Feature extraction
  • Land-use classification
  • Pretrained network
  • Remote sensing (RS)


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