An assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series

C. Pelletier, S. Valero, J. Inglada, G. Dedieu, N. Champion

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

5 Citations (Scopus)

Abstract

New high resolution Satellite Image Time Series (SITS) are becoming crucial to land cover mapping over large areas. Their high temporal resolution will allow to better depict scene dynamics. However, it will also increase the amount of data to process. The classification of these data involves therefore new challenges such as: (1) selecting the best feature set to use as input data, (2) dealing with data variability coming from landscape diversity, and (3) establishing the robustness of existing classifiers over large areas. This work aims at addressing these questions through three different studies. Experimental results are obtained by using SPOT-4 and Landsat-8 SITS.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Subtitle of host publicationJuly 10–15, 2016 Beijing, China
EditorsJiancheng Shi, Huadong Guo, Kun-shan Chen
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3338-3341
Number of pages4
ISBN (Electronic)9781509033324
ISBN (Print)9781509033331
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Geoscience and Remote Sensing Symposium 2016 - China National Convention Center, Beijing, China
Duration: 10 Jul 201615 Jul 2016
Conference number: 36th
https://www2.securecms.com/IGARSS2016/Default.asp
http://www.igarss2016.org/
https://ieeexplore.ieee.org/xpl/conhome/7592514/proceeding (Proceedings)

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium 2016
Abbreviated titleIGARSS 2016
CountryChina
CityBeijing
Period10/07/1615/07/16
Internet address

Keywords

  • Classification
  • High resolution
  • Land cover mapping
  • Random Forest
  • Satellite Image Time Series

Cite this