Image segmentation algorithms comparison

Reis, Mariane Souza; Oliveira, Maria Antonia Falcão de; Körting, Thales Sehn; Pantaleão, Eliana; Sant’Anna, Sidnei J. S.; Dutra, Luciano Vieira; Lu, Dengsheng

doi:10.1109/igarss.2015.7326787

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consider a reference segmentation with K segments and the evaluated segmentation with L segments, both with W columns and H rows, a given region i in the reference segmentation, and a given object in the segmentation being evaluated, f . The < gi > notation represents the average of the measure g in a given region i, N (i) represents the number of pixels of i and xi and yi indicate the location of some pixel inside i region, respectively the column and row (Reis et al, 2015). Two matrices, Gf and F it, both with H rows and W columns, are constructed using the following equations:…”

Section: Methodsmentioning

confidence: 99%

First experiments using the image foresting transform (IFT) algorithm for segmentation of remote sensing imagery

Soares¹,

Körting²,

Fonseca³

2016

GEOBIA 2016: Solutions and Synergies

View full text Add to dashboard Cite

Image segmentation is a traditional method in Remote Sensing and a fundamental problem in image processing applications. It has been widely used, especially with the emergence of the Geographic Object-Based Image Analysis (GEOBIA). The results of segmentation must create uniform areas, which must allow a simpler interpretation by the users and simpler representation for classification algorithms. Several algorithms were proposed through the years, using different approaches. One that is widely used in Remote Sensing applications is the Multiresolution algorithm, that is based on the region growing method. Other, which has great potential and is applied in other research areas, is available on the Image Foresting Transform (IFT) framework, which has several image operators developed primarily for medical images. The Watershed from Grayscale Marker operator uses an edge image to perform the segmentation, however, we propose an extension of the edge detection algorithm, by summing normalized gradients of each band. This work aims to evaluate and compare these two segmentation algorithms, by comparing their results through supervised segmentation from reference regions, that were defined manually by an expert user. Quality measures were evaluated by four metrics, that represent the positional adjustment based the center of gravity, intensities, size, and the amount of overlap between the segment created by the algorithms and the reference segment. We selected 21 objects of a WorldView-2 multispectral image that were used to compute the metrics. Both methods reached similar results, by comparing the aforementioned 4 metrics applied to the 21 reference regions, IFT achieved better results for majority of regions. The IFT generated segments with similar shape when compared with the references, and the multiresolution generated results with similar sizes and positional adjustments. It may be explained by the fact that IFT uses an edge image to perform the segmentation. Both algorithms obtained similar agreement for intensity.

show abstract

Section: Methodsmentioning

confidence: 99%

First experiments using the image foresting transform (IFT) algorithm for segmentation of remote sensing imagery

Soares¹,

Körting²,

Fonseca³

2016

GEOBIA 2016: Solutions and Synergies

View full text Add to dashboard Cite

show abstract

“…The region-growing MIRS algorithm was used to create multi-pixel object primitives based on spatial and spectral features [44,45]. The algorithm takes all individual pixels of the orthophoto mosaic as a starting point and merges similar adjacent regions considering a user-defined threshold, i.e., scale parameter, for the maximum internal heterogeneity within the features [30].…”

Section: Object-based Image Analysismentioning

confidence: 99%

Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity

Nuijten

Kooistra

Deyn

2019

Drones

View full text Add to dashboard Cite

Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.

show abstract

Image segmentation algorithms comparison

Cited by 2 publications

References 6 publications

First experiments using the image foresting transform (IFT) algorithm for segmentation of remote sensing imagery

First experiments using the image foresting transform (IFT) algorithm for segmentation of remote sensing imagery

Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity

Contact Info

Product

Resources

About