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


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
Number of pages13
ISBN (Print)9783030922726
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


Conference28th International Conference on Neural Information Processing, ICONIP 2021
CityVirtual, Online


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


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