计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (25): 168-170.

• 图形、图像、模式识别 • 上一篇    下一篇

遥感图像变化检测算法研究

余银峰1，贾振红1，覃锡忠1，杨 杰2，庞韶宁3   

  1. 1.新疆大学 信息科学与工程学院，乌鲁木齐 830046
    2.上海交通大学 图像处理与模式识别研究所，上海 200240
    3.奥克兰理工大学 知识工程与开发研究所，新西兰 奥克兰 1020
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2011-09-01 发布日期:2011-09-01

Change detection algorithm in satellite images

YU Yinfeng1，JIA Zhenhong1，QIN Xizhong1，YANG Jie2，PANG Shaoning3   

  1. 1.College of Information Science and Engineering，Xinjiang University，Urumqi 830046，China
    2.Institute of Image Processing and Pattern Recognition，Shanghai Jiaotong University，Shanghai 200240，China
    3.Knowledge Engineering and Discovery Research Institute，Auckland University of Technology，Auckland 1020，New Zealand
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-09-01 Published:2011-09-01

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摘要： 提出了一种基于非下采样Contourlet变换和脉冲耦合神经网络的无监督的不同时相的卫星影像的变化检测新算法。该算法将非下采样Contourlet变换和脉冲耦合神经网络这两种方法结合，将它应用于不同时相的卫星影像的变化检测。实验结果表明，与传统方法相比，对于高斯和斑点噪声，该算法具有更高的抗噪性能和检测精确度。

关键词: 非下采样Contourlet变换, 脉冲耦合神经网络, 无监督变化检测, 多时相卫星影像, 遥感

Abstract: An unsupervised change detection algorithm in multi-temporal satellite images based on nonsubsampled Contourlet transform and pulse coupled neural network is proposed.This method makes a combination of both nonsubsampled Contourlet transform and pulse coupled neural network，and applies it to change detection initially.Experimental results demonstrate that the proposed method has a higher stability and accuracy against Gaussian and speckle noise compared with traditional algorithms.

Key words: Nonsubsampled Contourlet Transform（NSCT）, Pulse Coupled Neural Network（PCNN）, unsupervised change detection, multi-temporal satellite images, remote sensing

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