Remote Sensing Image Scene Classification Using Bag of Convolutional Features

Gong Cheng, Zhenpeng Li, Xiwen Yao, Lei Guo, Zhongliang Wei

Research output: Contribution to journalArticlepeer-review

328 Scopus citations

Abstract

More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features. In this letter, we propose a novel feature representation method for scene classification, named bag of convolutional features (BoCF). Different from the traditional bag of visual words-based methods in which the visual words are usually obtained by using handcrafted feature descriptors, the proposed BoCF generates visual words from deep convolutional features using off-the-shelf convolutional neural networks. Extensive evaluations on a publicly available remote sensing image scene classification benchmark and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed BoCF method for remote sensing image scene classification.

Original languageEnglish
Article number8008758
Pages (from-to)1735-1739
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume14
Issue number10
DOIs
StatePublished - Oct 2017

Keywords

  • Bag of convolutional features (BoCF)
  • bag of visual words (BoVW)
  • convolutional neural networks (CNNs)
  • feature representation
  • scene classification

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