Volume I-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 389-394, 2012
https://doi.org/10.5194/isprsannals-I-3-389-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 389-394, 2012
https://doi.org/10.5194/isprsannals-I-3-389-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Jul 2012

23 Jul 2012

AN EVALUATION OF FEATURE LEARNING METHODS FOR HIGH RESOLUTION IMAGE CLASSIFICATION

P. Tokarczyk, J. Montoya, and K. Schindler P. Tokarczyk et al.
  • Institute of Geodesy and Photogrammetry, ETH Zürich, 8093 Zürich, Switzerland

Keywords: classification, land cover, feature, pattern, recognition

Abstract. Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.