Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2)

Witharana, Chandi; Civco, Daniel L.

doi:10.1016/j.isprsjprs.2013.11.006

Cited by 109 publications

(71 citation statements)

References 32 publications

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“…In fact, based on the results obtained in this specific work, a good segmentation would require more than 100 ROs. It is relevant that only around 30 ROs per class were used in previous segmentation quality studies [4,20] and that, in this case study, 30 ROs were not enough to guarantee stable results. Figure 3 shows, as already reported in Aguilar et al [11], that the ED2 metric can be characterized by a very good relationship with the visual quality of plastic-covered greenhouses segmentations.…”

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 96%

“…Indeed, in previous scientific works, only a reduced number of ROs were used to assess the segmentation accuracy (e.g., in [4,9,11,20], this number was less than 100). The improvement achieved through the use of a large RO input can be demonstrated in the chosen test area.…”

Section: Discussionmentioning

confidence: 99%

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AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery

Novelli¹,

Aguilar²,

Aguilar³

et al. 2017

Remote Sensing

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This letter presents the capabilities of a command line tool created to assess the quality of segmented digital images. The executable source code, called AssesSeg, was written in Python 2.7 using open source libraries. AssesSeg (University of Almeria, Almeria, Spain; Politecnico di Bari, Bari, Italy) implements a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2) and was tested on different satellite images (Sentinel-2, Landsat 8, and WorldView-2). The segmentation was applied to plastic covered greenhouse detection in the south of Spain (Almería). AssesSeg outputs were utilized to find the best band combinations for the performed segmentations of the images and showed a clear positive correlation between segmentation accuracy and the quantity of available reference data. This demonstrates the importance of a high number of reference data in supervised segmentation accuracy assessment problems.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 96%

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AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery

Novelli¹,

Aguilar²,

Aguilar³

et al. 2017

Remote Sensing

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“…Image segmentation that divides an image into smaller objects is the first step in object based approach. In the literature, multiresolution segmentation approach has been one of the most influential image segmentation algorithms (Witharana and Civco, 2014). It is a region growing algorithm that depends on grouping pixels of initial single pixels and produces homogeneous image objects (Baatz and Schäpe, 2000).…”

Section: Introductionmentioning

confidence: 99%

A Region-Based Multi-Scale Approach for Object-Based Image Analysis

Kavzoğlu

Erdemir

Tonbul

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4KEY WORDS: Object Based Image Analysis, Region-based scale, Classification, Multiresolution Segmentation, Estimation of Scale Parameter (ESP). ABSTRACT:Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features.Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than imagebased single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

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“…Existing studies on segmentations consider two kinds of evaluation approaches, i.e., supervised and unsupervised evaluations [39][40][41]. For the supervised evaluation, the ground truths of functional-zone boundaries are required.…”

mentioning

confidence: 99%

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Zhang

Wang

et al. 2018

Remote Sensing

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Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities-Beijing, Putian, and Zhuhai-and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

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Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2)

Cited by 109 publications

References 32 publications

AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery

AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery

A Region-Based Multi-Scale Approach for Object-Based Image Analysis

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

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