Scene classification with recurrent attention of VHR remote sensing images

Qi Wang, Shaoteng Liu, Jocelyn Chanussot, Xuelong Li

Research output: Contribution to journalArticlepeer-review

558 Scopus citations

Abstract

Scene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches.

Original languageEnglish
Article number8454883
Pages (from-to)1155-1167
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume57
Issue number2
DOIs
StatePublished - Feb 2019

Keywords

  • Attention
  • convolutional neural network (CNN)
  • deep learning
  • long short-term memory (LSTM)
  • recurrent neural networks (RNN)
  • remote sensing
  • scene classification

Fingerprint

Dive into the research topics of 'Scene classification with recurrent attention of VHR remote sensing images'. Together they form a unique fingerprint.

Cite this