A clustering algorithm with optimized multiscale spatial texture information: application to SAR image segmentation

doi:10.1080/01431161.2012.718455

2012-10-01

PRE

Licheng Jiao

Xiaohua Zhang

DOI：10.1080/01431161.2012.718455

原文链接

原文求助

摘要

En 中文

An image segmentation method based on optimized spatial texture information is proposed in this article. Spatial information, including the relative position of neighbouring pixels and texture features of the multiscale neighbourhood, is incorporated into the similarity measure of the fuzzy c-means (FCM) clustering algorithm, in which the Gaussian kernel is adopted to diminish the local incorrect segmentation. The FCM clustering is spatially adjusted and optimized by the particle swarm optimization (PSO) algorithm. The purpose of optimization is to obtain the appropriate control parameters influencing spatial information, which can improve segmentation results. Experimental results demonstrate that the proposed method achieves better segmentation performance and is capable of effectively segmenting synthetic images and synthetic aperture radar (SAR) images.

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

International Journal of Remote Sensing

IF:

2.6

论文数:

1.2W

被引数:

2.7W

机构

Xidian University

学者数:

2.4W

论文数: 1.9W

被引数: 9.7K

引用论文

Products of the Peptic Digestion of Human γG-Immunoglobulin^*

Biochemistry

IF0

2002-05-01

PREAI

Ralph Heimer; Sidney S. Schnoll; Anita Primack

Emergency Remote Teaching and Beyond

IF0

2021-01-01

PREAI

A Robust Fuzzy Local Information C-Means Clustering Algorithm 一种鲁棒的模糊局部信息C均值聚类算法

IEEE TRANSACTIONS ON IMAGE PROCESSING

IF13.7

2010-05-01

1.0K

PREAI

Krinidis, Stelios; Chatzis, Vassilios

Nanocrystalline silicon films as multifunctional material for optoelectronic and photovoltaic applications 纳米硅薄膜作为光电和光伏应用的多功能材料

Materials Science and Engineering: B

IF0

2006-10-01

PREAI

S. Pizzini; M. Acciarri; S. Binetti; D. Cavalcoli; A. Cavallini; D. Chrastina; L. Colombo; E. Grilli; G. Isella; M. Lancin; A. Le Donne; A. Mattoni; K. Peter; B. Pichaud; E. Poliani; M. Rossi; S. Sanguinetti; M. Texier; H. von Känel

Challenges in analyzing polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs) and dioxin-like polychlorinated biphenyls (dl-PCBs) in food and feed in the light of the considerable tolerable weekly intake reduction proposed by EFSA in 2018

IOP Conference Series: Earth and Environmental Science

IF0

2019-09-01