Lahnet: A convolutional neural network fusing low- A nd high-level features for aerial scene classification

Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

In this paper, we proposed an innovative end-to-end convolutional neural network (CNN), which is trained to learn how to fuse multi-level features for aerial scene classification. Instead of using only coarse semantic features as conventional CNNs, we resort to first hierarchically extracting dense highlevel features and then element-wise fusing them with lowlevel features to build a comprehensive feature representation, which contains not only high-level semantic information but also fine-grained low-level details, for scene classification. The network is evaluated on two broadly used aerial scene datasets, UCM and AID. The experimental results indicate that the proposed LAHNet performs superiorly compared to the existing benchmark methods. Furthermore, visualization of the fused features presents an intuitive illustration of the remarkable improvement.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4728-4731
Number of pages4
ISBN (Electronic)9781538671504
DOIs
StatePublished - 31 Oct 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/TerritorySpain
CityValencia
Period22/07/1827/07/18

Keywords

  • Aerial scene classification
  • Convolutional neural network (CNN)
  • Feature fusion

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