地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (8): 1069-1079.doi: 10.3724/SP.J.1047.2017.01069

• 遥感科学与应用技术 • 上一篇    下一篇

Landsat时序变化检测综述

汤冬梅(), 樊辉*(), 张瑶   

  1. 1. 云南大学国际河流与生态安全研究院,昆明 650091;2. 云南省国际河流与跨境生态安全重点实验室,昆明 650091
  • 收稿日期:2017-02-03 修回日期:2017-05-31 出版日期:2017-08-20 发布日期:2017-08-31
  • 通讯作者: 樊辉 E-mail:Tangdongmeii@126.com;fanhui@ynu.edu.cn
  • 作者简介:

    作者简介:汤冬梅(1991-),女,湖北襄阳人,硕士生,主要从事山地环境遥感研究。E-mail: Tangdongmeii@126.com

  • 基金资助:
    国家自然科学基金项目(41461017);国家重点研发计划课题(2016YFA0601601);云南省中青年学术技术带头人后备人才培育计划(2014HB005);云南大学青年英才培育计划

Review on Landsat Time Series Change Detection Methods

TANG Dongmei(), FAN Hui*(), ZHANG Yao   

  1. 1. Institute of International Rivers and Eco-Security, Yunnan University, Kunming, 650091, China;2. Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, China
  • Received:2017-02-03 Revised:2017-05-31 Online:2017-08-20 Published:2017-08-31
  • Contact: FAN Hui E-mail:Tangdongmeii@126.com;fanhui@ynu.edu.cn

摘要:

时序变化检测已成为当前Landsat数据主流的变化检测方法。本文从检测算法对比、时序数据构建和精度评价等方面对Landsat时序变化检测进行回顾和评述,进而提出Landsat时序变化检测当前所存在的问题,及其所面临的挑战。Landsat时序变化检测算法可大致归纳为轨迹拟合法、光谱-时间轨迹法、基于模型的方法3大类,这些算法大多基于森林扰动提出;变化检测常用指标有波段型、植被指数型、线性变换型、组合型4大类,每类指标的优势不同,可综合多类指标以更全面地检测不同扰动类型。尽管Landsat时序变化检测已取得长足发展,但仍然面临诸多挑战,其中最大挑战是缺少一致性的参考数据集进行变化检测精度评价。

关键词: Landsat影像, 时序数据, 变化检测, 检测指标

Abstract:

Change detection based on Landsat time series has become one of the most popular methods of remote sensing change detection. This paper reviews the status of Landsat time series change detection, including comparison of change detection algorithms, Landsat time series construction and accuracy assessment of change detection results. Major problems and challenges of performing Landsat time series change detection are presented. Landsat time series change detection algorithms can roughly be classified into three categories, i.e., trajectory fitting methods, spectral-temporal trajectory methods, and model-based methods. These algorithms are mostly developed based on forest disturbance. Only few of them were used to detect changes in other land use/land cover types (e.g. urban expansion). Their applications in other fields need further verification. In particular, the comparative study of those different algorithms should be strengthened, which would provide better guidance for users to select optimal detection methods in specific fields. These indices commonly used for Landsat time series change detection can be divided into four groups, including spectral band, vegetation index, linear transformation and their combinations. It is often suggested to combine the advantages of various indices to detect different disturbance types. Although change detection methods based on Landsat time series have developed rapidly, many challenges remain. Upon now, the lack of consistent reference data set for accuracy assessment of Landsat time series change detection is the most serious challenge. Confronted with new challenges, new approaches are needed to calibrate the time series change detection algorithms.

Key words: Landsat images, time series data, change detection, detection indices