Advances in Utilization of Hierarchical Representations in Remote Sensing Data Analysis

From LRDE

Abstract

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.

Documents

Bibtex (lrde.bib)

@InCollection{	  tochon.17.chapter,
  author	= {Guillaume Tochon and Mauro {Dalla Mura} and {Miguel-Angel}
		  Veganzones ans Silvia Valero and Philippe Salembier and
		  Jocelyn Chanussot},
  title		= {Advances in Utilization of Hierarchical Representations in
		  Remote Sensing Data Analysis},
  booktitle	= {Comprehensive Remote Sensing, 1st Edition},
  publisher	= {Elsevier},
  editor	= {Shunling Liang},
  year		= {2017},
  month		= nov,
  volume	= {2},
  chapter	= {5},
  pages		= {77--107},
  abstract	= {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.}
}