Superpixel-Based Roughness Measure for Multispectral Satellite Image Segmentation

Toro, César A. Ortiz; Martı́n, Carmen; Pedrero, Ángel Mario García; Ruiz, Ernestina Menasalvas

doi:10.3390/rs71114620

Cited by 21 publications

(23 citation statements)

References 41 publications

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“…where d c and d s represent the color and spatial distance between pixels I x i , y i , s p and I x j , y j , s p in the spectral band s p , B represents the set of spectral bands used, S is the sampling interval of the clusters centroids, and m controls the compactness of superpixels [36]. The color distance controls superpixels homogeneity, while the spatial distance forces superpixel compactness [36].…”

Section: Simple Linear Iterative Clustering (Slic) Superpixelsmentioning

confidence: 99%

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Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Csillik

2017

Remote Sensing

109

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Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptually meaningful entities that are derived from pixels using a low-level grouping process (superpixels). Firstly, we tested a new workflow for image segmentation of remote sensing data, starting the multiresolution segmentation (MRS, using ESP2 tool) from the superpixel level and aiming at reducing the amount of time needed to automatically partition relatively large datasets of very high resolution remote sensing data. Secondly, we examined whether a Random Forest classification based on an oversegmentation produced by a Simple Linear Iterative Clustering (SLIC) superpixel algorithm performs similarly with reference to a traditional object-based classification regarding accuracy. Tests were applied on QuickBird and WorldView-2 data with different extents, scene content complexities, and number of bands to assess how the computational time and classification accuracy are affected by these factors. The proposed segmentation approach is compared with the traditional one, starting the MRS from the pixel level, regarding geometric accuracy of the objects and the computational time. The computational time was reduced in all cases, the biggest improvement being from 5 h 35 min to 13 min, for a WorldView-2 scene with eight bands and an extent of 12.2 million pixels, while the geometric accuracy is kept similar or slightly better. SLIC superpixel-based classification had similar or better overall accuracy values when compared to MRS-based classification, but the results were obtained in a fast manner and avoiding the parameterization of the MRS. These two approaches have the potential to enhance the automation of big remote sensing data analysis and processing, especially when time is an important constraint.

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Section: Simple Linear Iterative Clustering (Slic) Superpixelsmentioning

confidence: 99%

“…The color distance controls superpixels homogeneity, while the spatial distance forces superpixel compactness [36]. SLIC is an adapted k-means clustering, but what makes it fast and computationally efficient is that SLIC does not compare each pixel with all pixels in the scene.…”

Section: Simple Linear Iterative Clustering (Slic) Superpixelsmentioning

confidence: 99%

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Csillik

2017

Remote Sensing

109

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“…In computer vision, using superpixels (Achanta et al, 2012) to speed up later-stage processing are becoming increasingly popular in many applications (Achanta et al, 2012;Neubert and Protzel, 2012;Van den Bergh et al, 2012). In remote sensing few studies have used superpixels (Thompson et al, 2010;Guangyun et al, 2015;Ortiz Toro et al, 2015;Vargas et al, 2015). Even so, they did not tackle the usage of superpixels in order to improve the computational efficiency of segmenting remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Improving the speed of multiresolution segmentation using slic superpixels

Csilik

Lang

2016

GEOBIA 2016: Solutions and Synergies

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The popular multiresolution segmentation (MRS) algorithm is time and memory consuming when dealing with large images because it uses the pixel-grid for the initial object representation. In this study, we have tested a new workflow for image segmentation of remote sensing data, starting the MRS (using the ESP2 tool) from the superpixel level (using SLIC superpixels) and aiming at dramatically reducing the amount of time and computational resources needed to automatically partition relatively large datasets of very high resolution (VHR) remote sensing images. Tests were done on Quickbird and WorldView-2 data and the results show that the proposed workflow outperforms the traditional approach (MRS starting from pixels). The computational time was reduced in all cases, the biggest improvement being from 5h 35min to 13 min, for a WorldView-2 scene with 8 bands and an extent of 12.2 million pixels. This also comes with a slight improvement of the geometric accuracy of the extracted objects. This approach has the potential to enhance the automation of big remote sensing data analysis and processing, especially when time is an important constraint.

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“…This has similarly been done from a remote sensing perspective for object-based image analysis (OBIA) (Blaschke, 2010). The use of superpixels in computer vision is increasingly popular, whereas only few studies in remote sensing consider superpixels (Acuña et al, 2016;Chen et al, 2016;Csillik, 2016;Ortiz Toro et al, 2015;Sahli et al, 2012;Thompson et al, 2010;Vargas et al, 2015;Zhang et al, 2015).…”

Section: Superpixels In Remote Sensingmentioning

confidence: 99%

“…In further studies that apply superpixels on remote sensing data, SLIC is equally considered as the most suitable superpixel approach (Csilik and Lang, 2016;Ortiz Toro et al, 2015;Sahli et al, 2012;Vargas et al, 2015). SLIC has rarely been applied to UAV data, or for object delineation in topographic or cadastral mapping.…”

Section: Superpixels In Remote Sensingmentioning

confidence: 99%

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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Superpixel-Based Roughness Measure for Multispectral Satellite Image Segmentation

Cited by 21 publications

References 41 publications

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Improving the speed of multiresolution segmentation using slic superpixels

Slic Superpixels for Object Delineation From Uav Data

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