期刊信息

  • 刊名： 河北师范大学学报（自然科学版）Journal of Hebei Normal University (Natural Science)
  • 主办： 河北师范大学
  • ISSN： 1000-5854
  • CN： 13-1061/N
  • 中国科技核心期刊
  • 中国期刊方阵入选期刊
  • 中国高校优秀科技期刊
  • 华北优秀期刊
  • 河北省优秀科技期刊

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基于残余谱方法的农村居民地遥感提取研究

  • (1.河北师范大学 地理科学学院，河北 石家庄 050024; 2.河北省环境变化遥感识别技术创新中心，河北 石家庄 050024)
  • DOI： 10.13763/j.cnki.jhebnu.nse.202405001

Study on Remote Sensing Extraction of Rural Residential Region Based on Spectral Residual Method

摘要/Abstract

摘要：

视觉注意模型通过模拟人类的视觉注意机制而得到图像中最容易引起注意的显著区域.残余谱( spectral residuals，SR )方法是视觉注意模型中的一种，通过分析自然图片在频率域中的log谱来检测图片的显著性目标.将残余谱方法引入到遥感影像中用于获取农村居民地信息，通过比较高分 1 号、资源 3 号和 WorldView-2 这 3 个不同传感器的卫星影像，试分析遥感影像中不同的波谱和影像不同的获取时间对居民地提取结果的影响.结果表明：卫星影像中 RGB 组合提取的居民地效果更好，近红外波段在残余谱方法提取居民地时具有消极影响；8月获取的卫星影像用于居民地的提取相比于4月效果更好；残余谱方法用于农村居民地的提取对传感器的选择无明显倾向性.

Abstract：

Visual attention model can get the most noticeable region from image by simulating human visual attention mechanism.Spectral residual( SR ) method is one of the visual attention models.The salient targets of pictures are detected by analyzing the log spectrum of natural pictures in frequency domain.In this paper，spectral residual method is employed to remote sensing imagery to obtain rural residential regions.By comparing satellite images of GF-1，ZY-3 and WorldView-2 these three different sensors，the effects of different spatial resolution and different acquisition times of satellite images on extracted results of residential regions are analyzed.The results show that the effect of residential regions extracted by RGB combination in satellite image is better，and near-infrared band has negative influence on spectral residual method when extracting residential regions.The satellite images acquired in August are better for residential regions extraction than those in April.The spectral residual method used for the extraction of rural residential regions has no obvious tendency to the selection of sensors in this experiment.

关键词

关键词： 残余谱方法 ; 遥感影像 ; 农村居民地 ; 视觉注意模型 ; 显著性目标
Key words： spectral residual method ; remote sensing image ; rural residential regions ; visual attention models ; salient target

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