Using colour, texture, and hierarchial segmentation for high-resolution remote sensing

Image segmentation can be performed on raw radiometric data, but also on transformed colour spaces, or, for high-resolution images, on textural features. We review several existing colour space transformations and textural features, and investigate which combination of inputs gives best results for the task of segmenting high-resolution multispectral aerial images of rural areas into its constituent cartographic objects such as fields, orchards, forests, or lakes, with a hierarchical segmentation algorithm. A method to quantitatively evaluate the quality of a hierarchical image segmentation is presented, and the behaviour of the segmentation algorithm for various parameter sets is also explored.