Superpixel Segmentation: An Evaluation

Stutz, David

doi:10.1007/978-3-319-24947-6_46

Cited by 27 publications

(31 citation statements)

References 19 publications

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“…Superpixels can be understood as a form of image segmentation, that oversegment the image in a short computing time. Comparisons to similar approaches that can be found in (Achanta et al, 2012;Csillik, 2016;Neubert and Protzel, 2012;Schick et al, 2012;Stutz, 2015;Stutz et al, 2017) have demonstrated their advantages: The outlines of superpixels have shown to adhere well to natural image boundaries, as most structures in the image are conserved (Neubert and Protzel, 2012;Ren and Malik, 2003). Furthermore, they allow to reduce the susceptibility to noise and outliers as well as to capture redundancy in images.…”

Section: Introductionmentioning

confidence: 95%

“…State-of-the-art superpixel approaches have been compared in (Achanta et al, 2012;Csillik, 2016;Neubert and Protzel, 2012;Schick et al, 2012;Stutz, 2015;Stutz et al, 2017) considering speed, memory efficiency, compactness of outlines, their ability to adhere to image boundaries and their impact on segmentation performance. Boundary adherence is often measured via boundary recall, indicating how many true edges are missed, and via undersegmentation, indicating to what extent superpixels exceed outlines of the reference data (Achanta et al, 2012;Neubert and Protzel, 2012).…”

mentioning

confidence: 99%

“…Boundary adherence is often measured via boundary recall, indicating how many true edges are missed, and via undersegmentation, indicating to what extent superpixels exceed outlines of the reference data (Achanta et al, 2012;Neubert and Protzel, 2012). The SLIC superpixel approach, belonging to the group of gradient-ascent-based approaches, appears as the best overall performer: the algorithm is low in processing time and produces compact and nearly uniform superpixels that are positively evaluated in terms of boundary recall and undersegmentation error (Achanta et al, 2012;Csillik, 2016;Neubert and Protzel, 2012;Schick et al, 2012;Stutz, 2015;Stutz et al, 2017).…”

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confidence: 99%

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Slic Superpixels for Object Delineation From Uav Data

Crommelinck

Bennett

Gerke³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Introductionmentioning

confidence: 95%

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Slic Superpixels for Object Delineation From Uav Data

Crommelinck

Bennett

Gerke³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Furthermore, they extended these works to a supervoxel library and build a benchmark named LIBSVX [31]. Stutz [32] compared 11 superpixels segmentation algorithms with three metrics: boundary recall, undersegmentation error and runtime. He evaluated the accuracy of superpixel segmentation that was proposed in past years but recent works are not included, such as [4] and [7].…”

Section: Introductionmentioning

confidence: 99%

Superpixel segmentation: A benchmark

Wang

Liu

Gao

et al. 2017

Signal Processing: Image Communication

128

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“…Other desirable property is the control over the amount and compactness (spatial homogeneity criterion) of generated superpixels, which allows to adapt superpixels to specific requirements for real-world applications (Stutz, 2015). In particular, the control over these factors permits to generate superpixels that better capture spatial coherent information (Schick et al, 2014).…”

Section: Superpixel Segmentationmentioning

confidence: 99%

Spatio-temporal Analysis of Agricultural Landscape Images: A Superpixel-based Approach

Pedrero¹,

Adolfo²

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In the last hundred years, the world population has tripled and is still growing dramatically but resources have remained the same, causing changes in food supply outlook. According to the Food and Agriculture Organization (FAO), global food production will need to grow by 70% in order to satisfy the food and feed demand of a population of 9 billion people by 2050. In the current scenario, with limited arable lands and water scarcity, this means a greater pressure than ever before on productive land; thus today's main challenge is to put agriculture on a more sustainable and productive long-term path. In this context, the study of the variability within plots represents an opportunity for improving agricultural management. For most farmers, agricultural plots are variable which implies that not all areas within them require the same management; for example: some areas have specific needs of water, fertilizers, or pesticides in order to be more profitable. Therefore, adjusting the management of the plots based on the variability within them opens a route to a variable farm management able to improve cost/yield relationship.New generation of optical remote sensors placed on aircraft, satellite platforms and drones, offers accessible and useful data of very-high resolution for monitoring and determining spatial variability of agricultural fields at plot level. However, the landscape complexity makes the manual analysis of the variability within an agricultural plot a highly time-consuming and expensive task. At the same time, it hinders considerably the monitoring of a high number of agricultural plots simultaneously. Moreover this very-high spatial resolution represents a challenge for traditional approaches of analysis based on pixels, unable to handle the withinclass spectral variability, intrinsic to this type of images. Therefore, it exists a critical need to develop methodologies for efficiently and automatically extracting and analyzing information from very-high resolution images, that would allow stakeholders to enable variable farm management. In this context, it is of particular interest to automatically detect region dynamics within the agricultural plots.The aim of this work is the development of a methodology for automatic generation of spatial and temporal information on the dynamics of agricultural land, particularly at plot level. In this sense, the study of agricultural scenes has been addressed through an object-based approach, exploring superpixel methods, which are seen as a link between the pixels of the image and objects of interest. To this end a superpixel method for multi-spectral images has been proposed. This method has been exploited in different applications at three different scales of interest:vii (i) to analyze fragmented agricultural scenes, (ii) to delineate agricultural plots, and (iii) to capture the internal variability of agricultural plots.The analysis of fragmented agricultural scenes has been approached by two methodologies. The first one focuses on providing a fram...

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Superpixel Segmentation: An Evaluation

Cited by 27 publications

References 19 publications

Slic Superpixels for Object Delineation From Uav Data

Slic Superpixels for Object Delineation From Uav Data

Superpixel segmentation: A benchmark

Spatio-temporal Analysis of Agricultural Landscape Images: A Superpixel-based Approach

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