An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images

Sümer, Emre; Türker, Mustafa

doi:10.1016/j.compenvurbsys.2013.01.004

Cited by 44 publications

(23 citation statements)

References 25 publications

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“…Many researchers implemented more forward-looking methods to extract shapes of the detected buildings (Karantzalos andParagios, 2009, Sirmacek et al, 2010). Further studies, which employ the advantages of multi-spectral information, solve the detection problem in a classification framework (Lee et al, 2003, Koc-San and Turker, 2014, Sumer and Turker, 2013. However, due to the complexity of shapes and variety of materials of human-made constructions, the image classification in urban areas is still complicated.…”

Section: Related Workmentioning

confidence: 99%

Building Extraction From Remote Sensing Data Using Fully Convolutional Networks

Bittner

Cui

Reinartz

2017

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

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ABSTRACT:Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM) using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.

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Section: Related Workmentioning

confidence: 99%

Building Extraction From Remote Sensing Data Using Fully Convolutional Networks

Bittner

Cui

Reinartz

2017

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

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“…When applied to GA parameter management, the typical principle is to increase the mutation rate and decrease the crossover rate when the algorithm is converging [22][23][24][25][26].…”

Section: Adaptable Genetic Algorithmmentioning

confidence: 99%

“…Hongbo et al [25] use the average fitness value in relation to the best fitness in the population and changes of the average and best fitness over several iterations to solve the crew grouping problem in military operations. This approach was adopted later for the detection of high-resolution satellite images [23] and for optimal wind-turbine micrositing [26]. Homayouni and Tang [27] propose the use of indicators such as the best value of the fitness function, the frequency of the chromosomes with the similar best value, and the percentage of the same chromosomes in the population.…”

Section: Adaptable Genetic Algorithmmentioning

confidence: 99%

Fuzzy-Logic Controlled Genetic Algorithm for the Rail-Freight Crew-Scheduling Problem

et al. 2017

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“…GA is a search optimization technique that mimics some of the processes of natural selection and evolution, and is usually used to generate solutions for optimization and search problems (Emre & Mustafa, 2013). …”

Section: Improved Fuzzy Genetic Algorithmmentioning

confidence: 99%

The optimization model of the vendor selection for the joint procurement from a total cost of ownership perspective

Pan

2015

JIEM

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Abstract:Purpose: This paper is an attempt to establish the mathematical programming model of the vendor selection for the joint procurement from a total cost of ownership perspective.Design/methodology/approach: Fuzzy genetic algorithm is employed to solve the model, and the data set of the ball bearings purchasing problem is illustrated as a numerical analysis.Findings: According to the results, it can be seen that the performance of the optimization model is pretty good and can reduce the total costs of the procurement.Originality/value: The contribution of this paper is threefold. First, a literature review and classification of the published vendor selection models is shown in this paper. Second, a mathematical programming model of the vendor selection for the joint procurement from a total cost of ownership perspective is established. Third, an empirical study is displayed to illustrate the application of the proposed model to evaluate and identify the best vendors for ball bearing procurement, and the results show that it could reduce the total costs as much as twenty percent after the optimization.

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An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images

Cited by 44 publications

References 25 publications

Building Extraction From Remote Sensing Data Using Fully Convolutional Networks

Building Extraction From Remote Sensing Data Using Fully Convolutional Networks

Fuzzy-Logic Controlled Genetic Algorithm for the Rail-Freight Crew-Scheduling Problem

The optimization model of the vendor selection for the joint procurement from a total cost of ownership perspective

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