SPT 3.1: A free software for automatic tuning of segmentation parameters in optical, hyperspectral and SAR images

Achanccaray, Pedro; Ayma, V. A.; Jiménez, Luis Ignacio; Bernabé, Sergio; Happ, Patrick Nigri; Costa, Gilson Alexandre Ostwald Pedro da; Feitosa, Raul Queiroz

doi:10.1109/igarss.2015.7326785

Cited by 8 publications

(13 citation statements)

References 7 publications

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“…It is important to set these parameters properly. There are some methods for a fact-based determination of these parameters, such as estimation of scale parameters (ESP) [30], optimized image segmentation [31], segmentation parameter tuner (SPT) [32], plateau objective function (POF) [33] or the work of Stumpf and Kerle (2011), who optimized segmentation through the optimal use of the derived object features in a random forest framework [34]. In FNEA, the color and the shape parameter work contrarily: The larger the weight of the color is, the better the spectral consistency of the resulting objects.…”

Section: Resultsmentioning

confidence: 99%

“…This includes methods that assess the geometric differences between the generated image objects and the ground truth data [39]. For this study, the SPT tool is used to evaluate the segmentation results with seven metrics, namely Hoover Index (H), Area-Fit-Index (AFI), Shape Index (SI), Rand Index (RI), F-measure (F), Segmentation Covering (C), Reference Bounded Segments Booster (RBSB) [32], as shown in Table 3. The ground truth data for the four experiments are shown in Table 4.…”

Section: Accuracy Evaluationmentioning

confidence: 99%

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An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery

Han

Yang

et al. 2018

Remote Sensing

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Abstract:Remote sensing (RS) image segmentation is an essential step in geographic object-based image analysis (GEOBIA) to ultimately derive "meaningful objects". While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA). Specifically, a minimum spanning tree (MST) algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR) algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the "reverse searching-forward processing" chain based on message passing interface (MPI) parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2) and different selected landscapes (residential/industrial, residential/agriculture) covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral), while the accuracy is comparable with that of the FNEA method.

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Section: Resultsmentioning

confidence: 99%

Section: Accuracy Evaluationmentioning

confidence: 99%

An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery

Han

Yang

et al. 2018

Remote Sensing

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“…Edge detection segmentation regionalizes an image by locating feature boundary elements, while region-growing algorithms make use of seed pixels that are grown into objects, based on a region's homogeneity (Fortin et al, 2000;Munoz et al, 2003;Blaschke et al, 2014). Many factors affect the quality of segmentation results (Moller et al, 2007), and much research has been done on improving image object delineation by considering different segmentation algorithms (Meinel & Neubert, 2004), tuning input parameters (Achanccaray et al, 2014;Achanccaray et al, 2015) and studying the effect that the spatial and spectral characteristics of the objects of interest have on the output (Lucieer & Stein, 2002;Burnett & Blaschke, 2003;Tian & Chen, 2007;Blaschke, 2010;Petropoulos et al, 2012). Effective validation techniques that quantify a segmentation result's ability to delineate objects of interest remain elusive (Lucieer & Stein, 2002;Benz et al, 2004;Clinton et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Segmentation parameter tuning (SPT) is a free software tool that automatically tests certain parameter values and compares the level of agreement between the segmented objects and a manually collected reference objects. The level of agreement between the segmentation output and reference objects are calculated for each parameter combination and referred to as segmentation goodness (Achanccaray et al, 2014;Achanccaray et al, 2015). The main purpose of the experiments was to determine to what extent edge and area metrics can be used to assess the output of segmentation algorithms and for selecting optimal input features and parameters.…”

Section: Introductionmentioning

confidence: 99%

Assessing edge and area metrics for image segmentation parameter tuning and evaluation

Meyer

Niekerk

2016

GEOBIA 2016: Solutions and Synergies

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Image segmentation algorithms allow the creation of variables that influence the attributes of the resulting objects. There is currently no set way of identifying the best segmentation parameters for a specific application. It would consequently be useful to calculate the accuracy of a particular segmentation for determining how well it can delineate an object of interest. This article assesses the use of both an area and edge metric to evaluate and quantify the ability of a segmentation algorithm to delineate objects of interest based on manually collected reference data. SPOT 5 imagery was used for the segmentation of two 2x3km study areas in the Eastern-Cape of South Africa. The aims of this study were to use the area metric to identify a scale parameter for which the ratio between oversegmentation (OS) and undersegmentation (US) yields the most favourable result and, use the edge metric to determine the accuracy to which the boundary of objects of interest is delineated. The usefulness of estimating scale parameter (ESP) and segmentation parameter tuner (SPT) are also investigated to identify the suitable scale parameter. The root mean square error (RMSE) between OS and US, was used in conjunction with the edge metric results to produce an overall accuracy value between 0-1. The ESP tool was found useful as a guide, but failed to identify the scale parameter yielding the most accurate results, whereas, the SPT tool succeeded in both cases to identify this scale parameter. It was concluded that the edge and area metric can be used in conjunction to, not only determine the ideal scale parameter, but also calculate the overall accuracy of the segmentation algorithm.

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“…There are some methods on automatic determination of appropriate segmentation parameters, such as Estimation of Scale Parameters (ESP) [43], Optimised image segmentation [44], SPT (Segmentation Parameter Tuner) [45], Plateau Objective Function [46]. In this study, the selection of image segmentation parameters is based on an iterative trial-and-error approach that is often utilized in object-based classification [6,10].…”

Section: Image Segmentationmentioning

confidence: 99%

An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology

et al. 2017

Remote Sensing

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Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIA-similar to other emerging paradigms-lacks formal expressions and objective modelling structures and in particular semantic classification methods using ontologies. This study has put forward an object-based semantic classification method for high resolution satellite imagery using an ontology that aims to fully exploit the advantages of ontology to GEOBIA. A three-step workflow has been introduced: ontology modelling, initial classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic rules. The classification part is based on data-driven machine learning, segmentation, feature selection, sample collection and an initial classification. Then, image objects are re-classified based on the ontological model whereby the semantic relations are expressed in the formal languages OWL and SWRL. The results show that the method with ontology-as compared to the decision tree classification without using the ontology-yielded minor statistical improvements in terms of accuracy for this particular image. However, this framework enhances existing GEOBIA methodologies: ontologies express and organize the whole structure of GEOBIA and allow establishing relations, particularly spatially explicit relations between objects as well as multi-scale/hierarchical relations.

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SPT 3.1: A free software for automatic tuning of segmentation parameters in optical, hyperspectral and SAR images

Cited by 8 publications

References 7 publications

An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery

An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery

Assessing edge and area metrics for image segmentation parameter tuning and evaluation

An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology

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