Tile2Vec with Predicting Noise for Land Cover Classification

Marshal Arijona Sinaga, Fadel Muhammad Ali, Aniati Murni Arymurthy

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

Abstract

Tile2vec has proven to be a good representation learning model in the remote sensing field. The success of the model depends on l2-norm regularization. However, l2-norm regularization has the main drawback that affects the regularization. We propose to replace the l2-norm with regularization with predicting noise framework. We then develop an algorithm to integrate the framework. We evaluate the model by using it as a feature extractor on the land cover classification task. The result shows that our proposed model outperforms all the baseline models.

Original languageEnglish
Title of host publicationNeural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
EditorsTeddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
PublisherSpringer Science and Business Media Deutschland GmbH
Pages87-99
Number of pages13
ISBN (Print)9783030922726
DOIs
Publication statusPublished - 2021
Event28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online
Duration: 8 Dec 202112 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13111 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on Neural Information Processing, ICONIP 2021
CityVirtual, Online
Period8/12/2112/12/21

Keywords

  • Deep learning
  • Land cover classification
  • Predicting noise
  • Remote sensing
  • Representation learning
  • Tile2vec

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