Remote Sensing Classification Using Fuzzy C-means Clustering with Spatial Constraints Based on Markov Random Field
نویسندگان
چکیده
منابع مشابه
Remote Sensing Classification Using Fuzzy C-means Clustering with Spatial Constraints Based on Markov Random Field
This paper proposes a new clustering algorithm which integrates Fuzzy C-means clustering with Markov random field (FCM). The density function of the first principal component which sufficiently reflects the class differences and is applied in determining of initial labels for FCM algorithm. Thus, the sensitivity to the random initial values can be avoided. Meanwhile, this algorithm takes into a...
متن کاملFuzzy c-Means Classification of Multispectral Data Incorporating Spatial Contextual Information by using Markov Random Field
Disclaimer This document describes work undertaken as part of a programme of study at the Indian Institute of Remote Sensing and International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute. Abstract Remote sensing technologies provide a un...
متن کاملCluster-Based Image Segmentation Using Fuzzy Markov Random Field
Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural inf...
متن کاملMultiresolution Fuzzy C-Means Clustering Using Markov Random Field for Image Segmentation
In this paper, an unsupervised multiresolution image segmentation algorithm is put forward, which combines interscale and intrascale Markov random field and fuzzy c-means clustering with spatial constraints. In the initial label determination of wavelet coefficient phase, the statistical distribution property of wavelet coefficients is characterized by Gaussian mixture model, the properties of ...
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 کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: European Journal of Remote Sensing
سال: 2013
ISSN: 2279-7254
DOI: 10.5721/eujrs20134617