A Geographical Approach to Self-Organizing Maps Algorithm Applied to Image Segmentation

Körting, Thales Sehn; Fonseca, Leila María García; Câmara, Gilberto

doi:10.1007/978-3-642-23687-7_15

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…the Baatz and Schape algorithm [37]: BS-S, where S is the varying scale parameter in GEODMA, see Figures 4 and 5; for most of the BS-S partitions, the color and the shape parameters are fixed; -Self Organizing Maps adapted to GEOBIA [38,39]: SOM-R-A-K, where R is the radius of the neighborhood Gaussian function of the neuron, A is alpha, the learning rate used to update the neural network and K is the radius of the geographical best matching unit to be searched ( Figure 6). Concerning the Baatz and Schäpe algorithm and for the resulting segmentations to be comparable, for most of the partitions, the color parameter is fixed to 40 and the shape parameter to 30, while the scale parameter S can change over a large range: 5, 6, 7, 10, 15, 20, 40, 50, 70, 100, 150, 200, 250.…”

Section: Materials and Datamentioning

confidence: 99%

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Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

Josselin¹,

Louvet²

2019

IJGI

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Using two GEOBIA (Geographical Object Based Image Analysis) algorithms on a set of segmented images compared to grid partitioning at different scales, we show that statistical metrics related to both objects and sets of pixels are (more or less) subject to the Modifiable Areal Unit Problem. Subsequently, even in a same spatial partition, there may be a bias in statistics describing the objects due to some size effect of the pixel samples. For instance, pixels homogeneity based on Grey Level Cooccurrence Matrices (GLCM), Landscape Shape Index, entropy, object compacity, perimeter/area ratio are studied according to scale. The approach consists in studying the behavior of a given statistical metrics through scales and to compare the results on several image segmentations, according to different partitioning processes, from GEOBIA (Baatz & Schäpe algorithm and Self Organizing Maps) or using reference grids. We finally discuss about the relationship between GEOBIA metrics and scale. By analysing object shape and pixels composition from different metrics points of views, we show that GEOBIA does not always mitigate the Modifiable Areal Unit Problem.

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Section: Materials and Datamentioning

confidence: 99%

“…Segmentation obtained using Self Organizing Map (SOM-2-2-15) with the following parameters: radius = 2, alpha = 2 and K = 15[39].…”

mentioning

confidence: 99%

Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

Josselin¹,

Louvet²

2019

IJGI

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“…• An algorithm based on [45], which classifies spectrally similar pixels according to their location in the feature space, using a geographic extension of the Self-Organizing Maps (SOM).…”

Section: Module "Segmentation"mentioning

confidence: 99%

GeoDMA—Geographic Data Mining Analyst

Körting

Fonseca

Câmara

2013

Computers & Geosciences

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Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in one year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical analysis, but up to now have had limited use in remote sensing image interpretation due to the lack of appropriate tools. The toolbox described in this paper is the Geographic Data Mining Analyst (GeoDMA). It has algorithms for segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis. GeoDMA uses decision-tree strategies adapted for spatial data mining. It connects remotely sensed imagery with other geographic data types using access to local or remote databases. GeoDMA has methods to assess the accuracy of simulation models, as well as tools for spatio-temporal analysis, including a visualization of time-series that helps users to find patterns in cyclic events. The software includes a new approach for analyzing spatio-temporal data based on polar coordinates transformation. This method creates a set of descriptive features that improves the classification accuracy of multi-temporal image databases. GeoDMA is tightly integrated with TerraView GIS, so its users have access to all traditional GIS features. To demonstrate GeoDMA, we show two case studies on land use and land cover change.

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Application of neural networks in detection of abnormal brain perfusion regions

Hachaj

Ogiela

2013

Neurocomputing

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A Geographical Approach to Self-Organizing Maps Algorithm Applied to Image Segmentation

Cited by 5 publications

References 15 publications

Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

GeoDMA—Geographic Data Mining Analyst

Application of neural networks in detection of abnormal brain perfusion regions

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