Change Detection from Unlabeled Remote Sensing Images Using SIAMESE ANN

Rachid Hedjam, Abdelhamid Abdesselam, Farid Melgani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this article, we propose a new semi-supervised method to detect changes occurring in a geographical area after a major event such as war, an earthquake or flood. The detection is made by processing a pair of bi-temporal remotely sensed images of the area under consideration. The proposed method adopts a patch-based approach, where successive pairs of patches from the input images are compared using a deep machine learning method trained with augmented data. Our main contribution consists of proposing an approach for generating a training dataset from unlabeled pair of input images. The genuine training patch-pairs are directly generated from the transformed maps of the image taken before the event, while the impostor patch-pairs are generated by pairing the image taken before the event with any images, from the Internet, with textures that resemble the change shown in the image taken after the event. Several experiments were conducted on pairs of images related to five major events. The obtained subjective results demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1530-1533
Number of pages4
ISBN (Electronic)9781538691540
DOIs
Publication statusPublished - Jul 1 2019
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: Jul 28 2019Aug 2 2019

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
CountryJapan
CityYokohama
Period7/28/198/2/19

Keywords

  • change detection
  • deep learning
  • remote sensing
  • Siamese neural networks
  • unlabeled data

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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