Classification of Remote Sensing Data with 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 کاملHigh-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field
As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying high-resolution remote sensing data. Specifically, the presented method proceeds in three m...
متن کاملSpherical Classification of Remote Sensing Data
Real data are often characterized by high dimensional feature vectors. However, such data contain redundant information that may not be beneficial for analysis algorithms. As such, feature transformation arises in related fields of study, including geoscientific applications, as a means to capture the few characteristics that are useful for pattern analysis algorithms. In this study, we investi...
متن کاملMarkov Random Fields for SAR Remote Sensing Applications
This article aims at illustrating the powerfulness of Bayesian and specially Markovian frameworks for different remote sensing applications and in particular for SAR (Synthetic Aperture Radar) image processing. Indeed, the Markovian model is a very convenient way to introduce prior knowledge on the problem to solve. It will first be evoked with examples on the pixel level like filtering, segmen...
متن کاملMarkov Random Field for Road Extraction Applications in Remote Sensing Images
Bayesian methods coupled with Markovian frameworks has several applications in remote sensing images processing, such as the pixel level applications like filtering, segmentation and classification, and the higher level applications like object recognition and organization etc. This article illustrates the powerfulness of Markovian model at two levels for the road extraction problem in remote s...
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: Journal of Applied Sciences
سال: 2010
ISSN: 1812-5654
DOI: 10.3923/jas.2010.636.643