Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition

Multi-resolution segmentation, as one of the most popular approaches in object-oriented image segmentation, has been greatly enabled by the advent of the commercial software, eCognition. However, the application of multi-resolution segmentation still poses problems, especially in its operational aspects. This paper addresses the issue of optimization of the algorithm-associated parameters in multi-resolution segmentation. A framework starting with the definition of meaningful objects is proposed to find optimal segmentations for a given feature type. The proposed framework was tested to segment three exemplary artificial feature types (sports fields, roads, and residential buildings) in IKONOS multi-spectral images, based on a sampling scheme of all the parameters required by the algorithm. Results show that the feature-type-oriented segmentation evaluation provides an insight to the decision-making process in choosing appropriate parameters towards a high-quality segmentation. By adopting these feature-type-based optimal parameters, multi-resolution segmentation is able to produce objects of desired form to represent artificial features.