Feature selection from high resolution remote sensing data for biotope mapping

• Main Authors: M. Bindel, S. Hese, C. Berger, C. Schmullius
• Format: Article
• Language: English
• Published: Copernicus Publications 2012-09-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXVIII-4-W19/39/2011/isprsarchives-XXXVIII-4-W19-39-2011.pdf
• ISSN: 1682-1750; 2194-9034

Mapping of Landscape Protection Areas with regard to user requirements for detailed land cover and biotope classes has been limited by the spatial and temporal resolution of Earth observation data. The synergistic use of new generation optical and SAR data may overcome these limitations. The presented work is part of the ENVILAND-2 project, which focuses on the complementary use of RapidEye and TerraSAR-X data to derive land cover and biotope classes as needed by the Environmental Agencies. The goal is to semi-automatically update the corresponding maps by utilising more Earth observation data and less field work derived information. Properties of both sensors are used including the red edge band of the RapidEye system and the high spatial and temporal resolution TerraSAR-X data.The main part of this work concentrates on the process of feature selection. Based upon multi-temporal optical and SAR data various features like textural measurements, spectral features and vegetation indices can be computed. The resulting information stacks can easily exceed hundreds of layers. The goal of this work is to reduce these information layers to get a set of decorrelated features for the classification of biotope types. The first step is to evaluate possible features. Followed by a feature extraction and pre-processing. The pre-processing contains outlier removal and feature normalization. The next step describes the process of feature selection and is divided into two parts. The first part is a regression analysis to remove redundant information. The second part constitutes the class separability analysis. For the remaining features and for every class combination present in the study area different separability measurements like divergence or Jeffries-Matusita distance are computed. As result there is a set of features for every class providing the highest class separability values. As the final step an evaluation is performed to estimate how much features for a class are needed to get the highest classification accuracy by employing an object-based classification approach and to assess how classification accuracy changes with various numbers of features. The study is carried out for two case studies: 1. Rostocker Heide; (Special Area of Conservation (SAC, EC Habitats Directive)), Mecklenburg-Vorpommern and 2. Elsteraue (Landscape Protection Area) near Groitzsch, Sachsen. Both test sites are located in Germany.