Remote sensing image segmentation by active queries

Devis Tuia

doi:10.1016/j.patcog.2011.12.012

Return

Remote sensing image segmentation by active queries

2012-06-01

PRE

Devis Tuia *

Jordi Muñoz-Marı́

Camps-Valls, Gustavo

DOI：10.1016/j.patcog.2011.12.012

Original

Original request for help

Save

Abstract

En 中文

Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical description of the data, the proposed strategy aims at sampling and labeling pixels in order to discover the data partitioning that best matches with the user's expected classes. Thus, the method combines active supervised and unsupervised clustering with a smart prune-and-label strategy. The proposed method is successfully evaluated in two challenging remote sensing scenarios: hyperspectral and very high spatial resolution (VHR) multispectral images segmentation. (C) 2011 Elsevier Ltd. All rights reserved.

Keywords:

Active learning

Clustering

Linkage

Multiscale image segmentation

Remote sensing

Hyperspectral imagery

Multispectral imagery

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.