Broad-classifier for remote sensing scene classification with spatial and channel-wise attention

Zhihua Chen, Yunna Liu, Han Zhang, Bin Sheng, Ping Li, Guangtao Xue

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Remote sensing scene classification is an important technology, which is widely used in military and civil applications. However, it is still a challenging problem due to the complexity of scene images. Recently, the development of remote sensing satellite and sensor devices has greatly improved the spatial resolution and semantic information of remote sensing images. Therefore, we propose a novel remote sensing scene classification approach to enhance the performance of scene classification. First, a spatial and channel-wise attention module is proposed to adequately utilize the spatial and feature information. Compare with other methods, channel-wise module works on the feature maps with diverse levels and pays more attention to semantic-level features. On the other hand, spatial attention module promotes correlation between foreground and classification result. Second, a novel classifier named broad-classifier is designed to enhance the discriminability. It greatly reduces the cost of computing in the meantime by broad learning system. The experimental results have show that our classification method can effectively improve the average accuracies on remote sensing scene classification data sets.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 37th Computer Graphics International Conference, CGI 2020, Proceedings
EditorsNadia Magnenat-Thalmann, Constantine Stephanidis, George Papagiannakis, Enhua Wu, Daniel Thalmann, Bin Sheng, Jinman Kim, Marina Gavrilova
PublisherSpringer Science and Business Media Deutschland GmbH
Pages267-275
Number of pages9
ISBN (Print)9783030618636
DOIs
Publication statusPublished - Oct 2020
Externally publishedYes
Event37th Computer Graphics International Conference, CGI 2020 - Geneva, Switzerland
Duration: 20 Oct 202023 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12221 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference37th Computer Graphics International Conference, CGI 2020
Country/TerritorySwitzerland
CityGeneva
Period20/10/2023/10/20

Keywords

  • Attention mechanism
  • Broad learning system
  • Remote sensing image
  • Scene classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Broad-classifier for remote sensing scene classification with spatial and channel-wise attention'. Together they form a unique fingerprint.

Cite this