Design of a spectral-spatial pattern recognition framework for risk assessments using landsat data

A case study in Chile

authored by: Andreas Christian Braun, Carolina Rojas, Cristian Echeverri, Franz Rottensteiner, Hans Peter Bähr, Joachim Niemeyer, Mauricio Aguayo Arias, Sergey Kosov, Stefan Hinz, Uwe Weidner
Abstract: For many ecological applications of remote sensing, traditional multispectral data with moderate spatial and spectral resolution have to be used. Typical examples are land-use change or deforestation assessments. The study sites are frequently too large and the timespan covered too long assumes the availability of modern datasets such as very high resolution or hyperspectral data. However, in traditional datasets such as Landsat data, separability of the relevant classes is limited. A promising approach is to describe the landscape context pixels that are integrated. For this purpose, multiscale context features are computed. Then, spectral-spatial classification is employed. However, such approaches require sophisticated processing techniques. This study exemplifies these issues by designing an entire framework for exploiting context features. The framework uses kernel-based classifiers which are unified by a multiple classifier system and further improved by conditional random fields. Accuracy on three scenarios is raised between 19.0%pts and 26.6%pts. Although the framework is designed, focusing an application in Chile, it is generally enough to be applied to similar scenarios.
Organisation(s): Institute of Photogrammetry and GeoInformation (IPI)
External Organisation(s): Karlsruhe Institute of Technology (KIT)
Universidad de Concepcion
Type: Article
Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume: 7
Pages: 917-928
No. of pages: 12
ISSN: 1939-1404
Publication date: 03.2014
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Computers in Earth Sciences, Atmospheric Science
Sustainable Development Goals: SDG 15 - Life on Land
Electronic version(s): https://doi.org/10.1109/JSTARS.2013.2293421 (Access: Closed)