测绘通报 ›› 2022, Vol. 0 ›› Issue (11): 39-43.doi: 10.13474/j.cnki.11-2246.2022.0322

• 学术研究 • 上一篇    下一篇

结合空间上下文与慢特征分析的多光谱影像变化检测

王晓雯, 戴晨光, 张振超, 季虹良   

  1. 信息工程大学地理空间信息学院, 河南 郑州 450001
  • 收稿日期:2022-07-06 发布日期:2022-12-08
  • 通讯作者: 戴晨光,E-mail:1755814325@qq.com
  • 作者简介:王晓雯(1998-),女,硕士生,研究方向为遥感影像变化检测。E-mail:chxywxw@163.com
  • 基金资助:
    国家自然科学基金(42071340)

Multispectral image change detection based on spatial context and slow feature analysis

WANG Xiaowen, DAI Chenguang, ZHANG Zhenchao, JI Hongliang   

  1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
  • Received:2022-07-06 Published:2022-12-08

摘要: 为了提高多光谱影像变化检测的精度,本文提出了一种结合空间上下文与慢特征分析的方法。首先采用自适应空间上下文提取算法围绕像素构建自适应区域,探索像素周围的上下文信息;然后通过迭代慢特征分析,由相应像素周围的成对自适应区域定量计算成对像素之间的变化强度,增强变化区域与未变区域的可分性;最后生成变化强度图像,采用大津阈值法作二值分类,将变化强度图划分为二值变化检测图。利用Landsat 7卫星ETM+传感器的图像,与4种基于代数的方法及基于变换的方法进行对比试验,结果表明,本文方法在降低漏检方面有所改善,提高了召回率。

关键词: 多光谱影像, 变化检测, 自适应空间上下文, 迭代慢特征分析

Abstract: To improve the accuracy of multispectral change detection, a method combining spatial context and slow feature analysis is proposed. First, an adaptive spatial context extraction algorithm is used to construct an adaptive region around the pixel to explore the context information around the pixel. Then through the iterative slow feature analysis, the change intensity between paired pixels is quantitatively calculated from the paired adaptive region around the corresponding pixel. The separability of the changed area and the unchanged area is enhanced. Finally, the change intensity image is generated, and the Otsu threshold method is used for binary classification, and the change intensity map is divided into binary change detection maps. Experiments use images from the Landsat 7 satellite TM sensor to compare with four algebra-based and transformation-based methods. The results demonstrate that the method in this paper performs better in terms of reducing omission errors and improving recall rate.

Key words: multispectral image, change detection, adaptive spatial context, iterative slow feature analysis

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