Dirk Tiede

TL;DR: In this paper, the authors discuss the limitations of prevailing per-pixel methods when applied to high-resolution images and explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition.

TL;DR: A tool, called estimation of scale parameter (ESP), that builds on the idea of local variance (LV) of object heterogeneity within a scene, enables fast and objective parametrization when performing image segmentation and holds great potential for OBIA applications.

TL;DR: A new automated approach to parameterising multi-scale image segmentation of multiple layers based on the potential of the local variance (LV) to detect scale transitions in geospatial data is introduced and implemented as a generic tool for the eCognition® software.

TL;DR: The CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner, Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the results of augmentation strategies to artificially increase the number of existing samples are better understanding.

TL;DR: Evaluating the effect of the advanced feature selection methods of popular supervised classifiers for the example of object-based mapping of an agricultural area using Unmanned Aerial Vehicle (UAV) imagery verified that feature selection for both classifiers is crucial for the evolving field of Object-based Image Analysis (OBIA).