Slic Superpixels for Object Delineation From Uav Data

Abstract

ABSTRACT:Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05 m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64%. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping.

Interactive Cadastral Boundary Delineation From Uav Data

“…The approach has shown to be applicable to UAV orthoimages of 0.05 m ground sample distance (GSD). Further, cadastral boundaries demarcated through physical objects often coincide with the outlines of SLIC superpixels [17]. While [17] is based on a Matlab implementation [35], this study applies a GRASS implementation [36].…”

“…Further, cadastral boundaries demarcated through physical objects often coincide with the outlines of SLIC superpixels [17]. While [17] is based on a Matlab implementation [35], this study applies a GRASS implementation [36]. The latter appears to have a better boundary adherence for smaller superpixels.…”

Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression

“…The area threshold is set based on the minimum object size and is acquired through a zero-parameter version of simple linear iterative clustering (SLICO). The SLICO is a spatially localized version of the k-means [43]. To initialize it, the k-cluster centers, which are located on a regular grid and spaced S pixels apart, are sampled [44].…”

Towards Innovative Geospatial Tools for Fit-for-Purpose Land Rights Mapping

“…Therefore the UAV data was reduced in resolution, which lead to a reduced localization quality (Crommelinck et al, 2017b). To improve the localization quality and to verify initially detected candidate boundaries is the aim of the proceeding workflow step: for line extraction, simple linear iterative clustering (SLIC) superpixels ( Figure 5) were found to coincide largely with object boundaries in terms of completeness and correctness (Crommelinck et al, 2017a). For contour generation, gPb contour detection and SLIC superpixels are combined and processed in a semi-automatic tool that allows a subsequent final delineation of cadastral boundaries.…”

“…Figure 3. Sequence of commonly applied workflow steps proposed in (Crommelinck et al, 2016) to extract objects related to those manifesting cadastral boundaries from highresolution optical sensor data For the first and second workflow step, state-of-the-art computer vision approaches have been evaluated separately and determined as efficient for UAV-based cadastral mapping (Crommelinck et al, 2017a;Crommelinck et al, 2017b). The third workflow step as well as its combination with the proceeding steps are designed and implemented in ongoing work.…”