An original multi-sensor approach to scale-based image analysis for aerial and satellite images

Winter, A.; Maître, H.; Cambou, N.; Legrand, E.

doi:10.1109/icip.1997.638729

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…The first one consists in merging the segmentation result of each band [13,24,14]. The second one tries to generalize the classical segmentation process of one-component images [27]. Some works have applied genetic algorithms (GA) to image processing [1] and to segmentation particularly [4,8,28,11].…”

Section: Introductionmentioning

confidence: 99%

Optimization-Based Image Segmentation by Genetic Algorithms

Chabrier¹,

Rosenberger²,

Emile³

et al. 2008

EURASIP Journal on Image and Video Processing

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Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for grey-levels or multi-components images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multi-components images.

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Section: Introductionmentioning

confidence: 99%

Optimization-Based Image Segmentation by Genetic Algorithms

Chabrier¹,

Rosenberger²,

Emile³

et al. 2008

EURASIP Journal on Image and Video Processing

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show abstract

“…[27,33]. Similar to this approach, the methods presented in [19,44,45,50] propose to estimate the salient scale of an image by considering extrema of various information theoretic operators in the linear scale space. For remote-sensing images, [29] has also proposed to rely on a linear scale-space to estimate a resolution invariant characteristic scale.…”

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confidence: 99%

Local Scale Measure from the Topographic Map and Application to Remote Sensing Images

Luo¹,

Aujol²,

Gousseau³

2009

Multiscale Model. Simul.

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In this paper, we propose a spatially accurate definition of scale for images. The proposed definition of local scale relies on the hierarchical structure of the topographic map, the set of all level lines of an image. Namely, a connected component of a level line is associated to each point of the image and the scale at this point is then computed from the corresponding shape. Level lines are selected under the assumption that the blur is uniform over the image, a realistic assumption for satellite images. First, we discuss the links between the proposed approach and recent scale definitions making use of the total variation flow or the Rudin-Osher-Fatemi model. This comparison shed some light on the relationships between morphological and variational approaches to scale definitions. Then we perform several experiments on synthetic and satellite images, and numerically compare our local scale definition with results using the linear scale space, variational or morphological approaches. These experiments suggest that the proposed method enables a robust and spatially accurate computation of local scales, without the need of complex parameter tuning.

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Image Analysis and Computer Vision: 1995

Rosenfeld

1996

Computer Vision and Image Understanding

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An original multi-sensor approach to scale-based image analysis for aerial and satellite images

Cited by 9 publications

References 8 publications

Optimization-Based Image Segmentation by Genetic Algorithms

Optimization-Based Image Segmentation by Genetic Algorithms

Local Scale Measure from the Topographic Map and Application to Remote Sensing Images

Image Analysis and Computer Vision: 1995

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