Enhanced evaluation of image segmentation results

Marpu, Prashanth Reddy; Neubert, Marco; Herold, Hendrik; Niemeyer, Irmgard

doi:10.1080/14498596.2010.487850

Cited by 110 publications

(78 citation statements)

References 9 publications

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“…An empirical discrepancy method [31,45], which uses manually identified regions as reference objects, was adopted to quantitatively assess segmentation quality. First, clearly separable areas were manually segmented as the reference objects.…”

Section: Quantitative Assessment Methodsmentioning

confidence: 99%

“…However, scale as a threshold for MC often leads to similarly sized segments [22], but the real world is more complicated and contains objects with a large variation in size. Partitioning the image into segments similar in size may simultaneously cause over-segmentation and under-segmentation at a specific scale [31]. The solution to this problem, as offered by the multi-scale approach, is to segment images into differently scaled segmented layers that are linked by an object relation tree; this technique is known as the Fractal Net Evaluation Approach (FNEA) [32].…”

Section: Introductionmentioning

confidence: 99%

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Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

Chen

Qiu²,

et al. 2015

Remote Sensing

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Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD) and the edge penalty (EP). First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition's multi-scale segmentation.

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Section: Quantitative Assessment Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

Chen

Qiu²,

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

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“…Instead we will focus on approaches that have achieved demonstrated success in remote sensing land categorization applications. A compilation of such approaches can be found in a series of papers published by a research group based at the Leibniz Institute for Ecological and Regional Development (IOER) that present comparative evaluations of image segmentation approaches implemented in various image analysis packages (Meinel and Neubert, 2004), (Neubert, et al, 2006), (Neubert, et al, 2008), (Marpu, et al, 2010). The next several sections describe various spatially-based image segmentation approaches, starting with region growing algorithms, and continuing with texture-based algorithms, morphological algorithms, graph-based algorithms, and MRF-based algorithms.…”

Section: Spatially-based Segmentation Approachesmentioning

confidence: 99%

Gaze Direction Estimation Method Using Support Vector Machines (SVMs)

2009

Journal of Institute of Control, Robotics and Systems

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“…Many authors state that the quality of segmentation process directly affects the resulting classification accuracy (Kim et al, 2009;Marpu et al, 2010). It can be stated that similarity of created objects with the selected scale parameter(s) to real-world objects is of considerable importance.…”

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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Enhanced evaluation of image segmentation results

Cited by 110 publications

References 9 publications

Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

Gaze Direction Estimation Method Using Support Vector Machines (SVMs)

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

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