The edge detection enhancement on satellite image using bilateral filter

Fawwaz, Insidini; Zarlis, Muhammad; Suherman, Suherman; Rahmat, R F

doi:10.1088/1757-899x/308/1/012052

Cited by 14 publications

(3 citation statements)

References 9 publications

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“…Image filtering is a set of methods in which the ground features are improved by conducting a complete separation of the boundaries that represent the ground targets to be detected (Fawwaz et al, 2018). The main objective of the filtering techniques is to process a specific image so that the result is more appropriate than the original image, and the application of processing is depended on the target to be detected.…”

Section: Image Filteringmentioning

confidence: 99%

Extractions Morphotectonic Features Using Satellite Data Processing and DEM-Derived Spatial Models

Thannoun

Adeeb

Al-Jawadi

2022

IGJ

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Morphotectonic features were investigated in the Shireen anticline, which is located in the Kurdistan region of northern Iraq to get more insight into the effect of the tectonic activity on this anticline. For deducing and illustrating the morphotectonic features of the anticline, some digital processing methods have been applied to the part of the image of Landsat in which this anticline appears, and using the Digital Elevation Model. The results explained the identification of such morphotectonic features such as faults, joints, and flat irons topography, and then updated the structural style of the anticline by identifying and drawing the inferred morphotectonic features. The tectonic activity was evaluated in terms of applying longitudinal profiles analysis to some selected valleys in both limbs of the anticline. Consequently, the prominence of knick points in the bottoms of the selected valleys reflects the existence of sudden slopes. Therefore, the southwestern limb of the anticline was suffered more tectonic uplifting activity, and this is clear from the appearance of the triangular shapes in the central part of this limb towards the southeastern plunge of the anticline.

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Section: Image Filteringmentioning

confidence: 99%

Extractions Morphotectonic Features Using Satellite Data Processing and DEM-Derived Spatial Models

Thannoun

Adeeb

Al-Jawadi

2022

IGJ

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“…Image preprocessing method is carried out to reduce any disparities between the bi-temporal source images that may arise due to the variations in atmospheric conditions at the time of image acquisition [32,33]. Since the images are acquired by the same sensor, image registration is not necessary.…”

Section: Image Preprocessingmentioning

confidence: 99%

Deep Feature Extraction and Feature Fusion for Bi-temporal Satellite Image Classification

Asokan¹,

Anitha²,

Pătruţ³

et al. 2020

Computers, Materials &Amp; Continua

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Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features. Then the fused image is classified into change and unchanged regions. The presence of mixed pixels in the bitemporal satellite images affect the classification accuracy due to the misclassification errors. The proposed method was compared with six state-of-theart change detection methods and analyzed. The main highlight of this method is that by taking into account the spatio-spectral and textural information in the input channels, the mixed pixel problem is solved. Experiments indicate the effectiveness of this method and demonstrate that it possesses low misclassification errors, higher overall accuracy and kappa coefficient.

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“…[12] pro-poses using the median filter to replace Gaussian Filter on the filtering step and Frei-Chen algorithm and Ostu-Algorithm will be used to calculate gradient amplitude. Ac-cording [14], Bilateral Filter will replace the Gaussian Filter on CEDA which provide better performance on noise removal. [16] proposes replace the Gaussian Filter with LoG Filter to preserve more detail of the image.…”

Section: A Previous Workmentioning

confidence: 99%

Improvement of the Traditional Canny Edge Detection Algorithm by using Combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter

Soon*¹,

Abas²,

Rahmalan³

2019

IJITEE

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Canny Edge Detection Algorithm was very popular on the computer vision area which used to preserve the edges of the image. Due to the defect of the Canny Edge Detection Algorithm like no efficiency on noise removal, some improvement on the Canny Edge Detection Algorithm was done by the researchers. On this paper, a new enhanced Canny Edge Detection Algorithm will be propose which replaces the Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter. The replace of Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter is to improve the performance of Canny Edge Detection Algorithm on noise removal. A comparison between Canny Edge Detection Algorithm proposed by this paper, Canny Edge Detection Algorithm proposed by (Ilkin, Tafralı, &Sahin, 2017) and traditional Canny Edge Detection Algorithm will be done. The comparison will done by using eight images with different type and size which corrupted by noise. The performance of three algorithms will be determined by using the Peak Signal to Noise Ratio (henceforth, PSNR) value which uses as a quantitative measure. From the result, the Canny Edge Detection proposed by this paper will provide a better performance on noise removal and which will give a better impact on preserve the edges of the images corrupted by noise.

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The edge detection enhancement on satellite image using bilateral filter

Cited by 14 publications

References 9 publications

Extractions Morphotectonic Features Using Satellite Data Processing and DEM-Derived Spatial Models

Extractions Morphotectonic Features Using Satellite Data Processing and DEM-Derived Spatial Models

Deep Feature Extraction and Feature Fusion for Bi-temporal Satellite Image Classification

Improvement of the Traditional Canny Edge Detection Algorithm by using Combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter

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