The comparison index: A tool for assessing the accuracy of image segmentation

Möller, Markus; Lymburner, Leo; Völk, Martin

doi:10.1016/j.jag.2006.10.002

Cited by 185 publications

(116 citation statements)

References 27 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…Local and global validation according to [18] was carried out against 2,235 digitized fields to select the best segmentation settings. First each reference field and the corresponding image objects were considered (local validation).…”

Section: Segmentation Of Agricultural Fields Using Pan-sharpened Spotmentioning

confidence: 99%

“…The challenge is to find the optimal segmentation parameters, as to derive image objects which resemble real world objects as closely as possible. In most remote sensing studies, segmentations have been evaluated, in order to avoid over-or under-segmentation, either visually [15] or by using complex (mathematical) approaches [17,18]. With regard to the subsequent classification simple and practicable quality estimation metrics are highly desirable.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

et al. 2010

View full text Add to dashboard Cite

Abstract:The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5-5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15-30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm. OPEN ACCESSRemote Sens. 2010, 2 1036

show abstract

Section: Segmentation Of Agricultural Fields Using Pan-sharpened Spotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

et al. 2010

View full text Add to dashboard Cite

show abstract

“…There are many approaches in calculating the accuracy assessment of segmentation based on literature but the most common method used is by visual Previous researchers have used different type of segmentation validation by overlapping the area that intersect between output segmented area with reference area (Möller et al, 2007). Another researcher used the distance between two centroids to assess the segmentation accuracy (Ke, Quackenbush, & Im, 2010).…”

Section: Crown Projection Area (Cpa) Delineation and Validationmentioning

confidence: 99%

“…On the other hand , Gougeon (1995) had developed the segmentation validation using 1:1 spatial corresponding based on goodness of fit (D). However, Clinton et al, (2010) had improve the segmentation validation method developed by Ke et al, (2010) and Möller et al, (2007) by modifying the relative area metrics by calculating Equation (3) and (4) and measuring the goodness of fit by using Equation (5).…”

Section: Crown Projection Area (Cpa) Delineation and Validationmentioning

confidence: 99%

Modelling the Carbon Stocks Estimation of the Tropical Lowland Dipterocarp Forest Using Lidar and Remotely Sensed Data

Zaki¹,

Latif²,

Suratman³

et al. 2016

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

View full text Add to dashboard Cite

ABSTRACT:Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear re gression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland Dipterocarp forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respecti vely, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland Dipterocarp forest.

show abstract

“…However, segmented objects from different image often vary geometrically due to some factors including illumination conditions, view angles and meteorological conditions [34]. Additionally, the segmentation process sometimes suffers from under-segmentation and over-segmentation errors, which may create objects that do not accurately represent real-world feature [35]. Thus, uncertainty in the object-based methods exists inevitably.…”

Section: Introductionmentioning

confidence: 99%

A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images

et al. 2016

View full text Add to dashboard Cite

Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to increase CD accuracy. First, two temporal images are stacked and segmented into multiscale segmentation maps. Then, a pixel-based change map with memberships belonging to changed and unchanged parts is obtained by fuzzy c-means clustering. Finally, based on the Dempster-Shafer evidence theory, the proposed scale-driven CD method incorporating uncertainty analysis is performed on the multiscale segmentation maps and the pixel-based change map. Two experiments were carried out on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and SPOT 5 data sets. The ratio of total errors can be reduced to 4.0% and 7.5% for the ETM+ and SPOT 5 data sets in this study, respectively. Moreover, the proposed approach outperforms some state-of-the-art CD methods and provides an effective solution for CD.

show abstract

The comparison index: A tool for assessing the accuracy of image segmentation

Cited by 185 publications

References 27 publications

Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

Modelling the Carbon Stocks Estimation of the Tropical Lowland Dipterocarp Forest Using Lidar and Remotely Sensed Data

A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images

Contact Info

Product

Resources

About