A Proficient Segmentation of Remote Sensing Images using Modified Kernel Fuzzy C-Means Algorithm
نویسندگان
چکیده
Images are imitations of factual world substances. Processing it to get better visualization is called as image processing. With the increasing availability and decreasing cost of satellite imagery, the Remote sensing image enhancement, segmentation and classification has become the most important research issue in field of Remote sensing. In this proposed work, Land sat 7 Remote Sensing images are considered. Initially the enhancement of satellite image is done using image enhancement techniques. Then the segmentation of satellite images has been done using Expectation Maximization(EM), Kernel-Means(K-Means), Kernel Fuzzy C-Means(KFCM) and Modified Kernel Fuzzy C-Means (MKFCM) algorithms. Results are obtained for different Land sat 7 Remote Sensing images. Finally quality measures such as mean square error, average difference, normalized cross correlation and error measurements like Peak signal to noise ratio, Normalized absolute error are calculated. KeywordsImage Enhancement, EM, K-Means, KFCM, MKFCM Algorithm.
منابع مشابه
Segmentation Improvement of High Resolution Remote Sensing Images based on superpixels using Edge-based SLIC algorithm (E-SLIC)
The segmentation of high resolution remote sensing images is one of the most important analyses that play a significant role in the maximal and exact extraction of information. There are different types of segmentation methods among which using superpixels is one of the most important ones. Several methods have been proposed for extracting superpixels. Among the most successful ones, we can r...
متن کاملHigh Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation
Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...
متن کاملHigh Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation
Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...
متن کاملA Novel Fuzzy-C Means Image Segmentation Model for MRI Brain Tumor Diagnosis
Accurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In this research, a novel fuzzy approach has been proposed to classify a given MRI brain image as normal or cancer label and the i...
متن کاملA fusion of remote sensing images segmentation based on Markov random fields and fuzzy c-means models
Remote sensing images segmentation is a challenging task in analysis process of terrestrial applications. In this paper, we propose a combination of two segmentation methods of remote sensing images. The first based on MRF (Markov Random Fields) method which takes into account the neighboring labels of the pixels and the second is computed with a Fuzzy C-means technique to improve the likelihoo...
متن کامل