Spatial Classification Using Fuzzy Membership Models

Authors:
J. T. Kent;K. V. Mardia
Affiliations:
Univ. of Leeds, Leeds, UK;Univ. of Leeds, Leeds, UK
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1988

Citing 2
Cited 10

Multi-dimensional multivariate Gaussian Markov random fields with application to image processing

Journal of Multivariate Analysis
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms

Spectral Mixture Analysis: Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data

Journal of Mathematical Imaging and Vision
Spectral Mixture Analysis: Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data

International Journal of Computer Vision - Joint special issue on image analysis
Fuzzy expert systems architecture for image classification using mathematical morphology operators

Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets

Fuzzy Sets and Systems
The Moments of the Mixel Distribution and Its Application to Statistical Image Classification

Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Hierarchical Bayesian Classification of Multimodal Medical Images

MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy pairwise Markov chain to segment correlated noisy data

Signal Processing
Fuzzy c-means approach to tissue classification in multimodal medical imaging

Information Sciences: an International Journal
Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model

Pattern Recognition

Quantified Score

Hi-index	0.14

Visualization

Abstract

In the usual statistical approach to spatial classification, it is assumed that each pixel belongs to precisely one of a small number of known groups. This framework is extended to include mixed-pixel data; then, only a proportion of each pixel belongs to each group. Two models based on multivariate Gaussian random fields are proposed to model this fuzzy membership process. The problems of predicting the group membership and estimating the parameters are discussed. Some simulations are presented to study the properties of this approach, and an example is given using Landsat remote-sensing data.