論文

2017年1月1日

Advances in utilization of hierarchical representations in remote sensing data analysis

Comprehensive Remote Sensing
  • G. Tochon
  • ,
  • M. Dalla Mura
  • ,
  • M. A. Veganzones
  • ,
  • S. Valero
  • ,
  • P. Salembier
  • ,
  • J. Chanussot

1-9
開始ページ
77
終了ページ
107
記述言語
掲載種別
論文集(書籍)内論文
DOI
10.1016/B978-0-12-409548-9.10340-9

© 2018 Elsevier Inc. All rights reserved. The latest developments in sensor design for remote sensing and Earth observation purposes are leading to images always more complex to analyze. Low-level pixel-based processing is becoming unadapted to efficiently handle the wealth of information they contain, and higher levels of abstraction are required. Region-based representations intend to exploit images as collections of regions of interest bearing some semantic meaning, thus easing their interpretation. However, the scale of analysis of the images has to be fixed beforehand, which can be problematic as different applications may not require the same scale of analysis. On the other hand, hierarchical representations are multiscale descriptions of images, as they encompass in their structures all potential regions of interest, organized in a hierarchical manner. Thus, they allow to explore the image at various levels of details and can serve as a single basis for many different further processings. Thanks to its flexibility, the binary partition tree (BPT) representation is one of the most popular hierarchical representations, and has received a lot of attention lately. This article draws a comprehensive review of the most recent works involving BPT representations for various remote sensing data analysis tasks, such as image segmentation and filtering, object detection or hyperspectral classification, and anomaly detection.

リンク情報
DOI
https://doi.org/10.1016/B978-0-12-409548-9.10340-9
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078650922&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85078650922&origin=inward

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