Search Results

Now showing 1 - 4 of 4

Some of the metrics are blocked by your
consent settings
SAR segmentation using anisotropic diffusion
(2008-01-01)
Intajag, Sathit
;
Tipsuwanporn, Vittaya
;
Cheevasuwit, Fusak
Speckle effects are commonly observed in synthetic aperture radar (SAR) images. The human eye is capable of deriving meaningful information from SAR images; however, an automatic or semi-automatic processing algorithm has difficulty in distinguishing objects in the images because of noise effects present in those images. This paper presents a segmentation method for SAR images, which employs an anisotropic diffusion algorithm. In the proposed scheme, a SAR image is transformed into a logarithmic domain where the diffusion process is used to grow homogeneous regions in the noise environment until the regions reach some criteria for homogeneity; consequently, the segmented image in the logarithm domain is converted to the intensity domain by an exponential function. To grow homogeneous regions the adaptive diffusion method is introduced with a tensor technique in which tensor data are varied with the neighboring pixels. The diffusion algorithm will stop itself by a standard deviation divided by the mean, which is provided according to the homogeneity criteria. Results are shown on both synthetic and satellite SAR images. The evaluation of the proposed method employs the theoretical gain of equivalent numbers of looks (ENL). © 2008 The Institute of Electrical Engineers of Japan.
Some of the metrics are blocked by your
consent settings
SAR segmentation using anisotropic diffusion
(2005-12-01)
Intajag, Sathit
;
Tipsuwanpon, Vittaya
Speckle effects are commonly observed in a synthetic aperture radar (SAR) image, which applies in many land applications, such as agriculture or forestry. However, although the human eye is able to derive the meaningful information from the SAR image, automatic processing is very difficult to distinguish the objects in the images because of the noise effect present in the image. This paper presents a segmentation method of the SAR images, which uses an anisotropic diffusion algorithm with an inherence region-growing algorithm. Anisotropic diffusion is modified in logarithmic domain to remove the speckle noise and to identify the homogeneous regions in ERS and JERS satellite data. The modified diffusion method is formulated with a tensor technique, varying with both the location of a pixel in the image and the orientation of the local image geometry vicinity the pixels. In our algorithm, SAR images are transformed into logarithmic domain and using the modified anisotropic diffusion to grow the homogeneous regions in the noise environment; consequently, the segmented image in the logarithm domain is converted to the intensity domain by an exponential function. On the other hand, our diffusion algorithm can stop itself by a standard deviation to mean criteria. Simulation results on the satellite images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise almost as well as that of the additive noise. The evaluation of the proposed method employs the theoretical gain in equivalent numbers of looks (ENL). © 2005 SICE.
Some of the metrics are blocked by your
consent settings
Anisotropic diffusion in synthetic aperture radars
(2005-12-01)
Intajag, Sathit
;
Tipsuwanpon, Vittaya
;
Cheevasuwit, Fusak
Speckle effects are commonly observed in synthetic aperture radar (SAR) image, which applies in many land applications, such as agriculture or forestry. However, although the human eye is able to derive the meaningful information from the SAR image, automatic processing is very difficult because of the noise effect present in the image. In this paper, anisotropic diffusion is modified in logarithmic domain to remove the speckle noise in ERS and JERS satellite data. The modified diffusion method is employed as a tensor, varying with both the location of a pixel in the image and the orientation of the local image geometry vicinity the pixel. In our algorithm, SAR images are transformed into logarithmic domain and using the modified anisotropic diffusion to smooth out the noise; consequently, the smoothed image is converted by an exponential function. Simulation results on the satellite images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise almost as well as that of the additive noise. The evaluation of the proposed method employs the theoretical gain in equivalent numbers of looks (ENL). © 2005 IEEE.
Some of the metrics are blocked by your
consent settings
Automated Edge Detection by a Fuzzy Morphological Gradient
(2003-01-01)
Intajag, Sathit
;
Paithoonwatanakij, Kitti
Edge detection has been an essential step in image processing, and there has been much work undertaken to date. This paper inspects a fuzzy mathematical morphology in order to reach a higher-level of edge-image processing. The proposed scheme uses a fuzzy morphological gradient to detect object boundaries, when the boundaries are roughly defined as a curve or a surface separating homogeneous regions. The automatic edge detection algorithm consists of two major steps. First, a new version of anisotropic diffusion is proposed for edge detection and image restoration. All improvements of the new version use fuzzy mathematical morphology to preserve the edge accuracy and to restore the images to homogeneity. Second, the fuzzy morphological gradient operation detects the step edges between the homogeneous regions as object boundaries. This operation uses geometrical characteristics contained in the structuring element in order to extract the edge features in the set of edgeness, a set consisting of the quality values of the edge pixels. This set is prepared with fuzzy logic for decision and selection of authentic edge pixels. For experimental results, the proposed method has been tested successfully with both synthetic and real pictures.
1

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results