地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 606-614.doi: 10.3724/SP.J.1047.2016.00606

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多源多时相遥感影像相对辐射归一化方法研究

黄启厅1,2(), 覃泽林3, 曾志康3   

  1. 1. 中国科学院遥感与数字地球研究所 遥感科学国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 广西农业科学院农业科技信息研究所,南宁 310001
  • 收稿日期:2015-12-16 修回日期:2016-04-06 出版日期:2016-05-10 发布日期:2016-05-10
  • 作者简介:

    作者简介:黄启厅(1983-),男,广西南宁人,博士生,研究方向为遥感信息提取。E-mail:huangqiting830112@163.com

  • 基金资助:
    国家高技术研究发展计划项目(2015AA123901);广西科学研究与技术开发计划项目(14125008-1-6);国家高分辨率对地观测系统重大专项(03-Y30B06-9001-13/15-01)

A Study on the Relative Radiometric Normalization of Multi-sources and Multi-temporal Remote Sensing Data

HUANG Qiting1,2,*(), QIN Zelin3, ZENG Zhikang3   

  1. 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
    3.Scientific and Technological Information Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
  • Received:2015-12-16 Revised:2016-04-06 Online:2016-05-10 Published:2016-05-10
  • Contact: HUANG Qiting E-mail:huangqiting830112@163.com

摘要:

为满足多源、多时相遥感影像定量化信息提取的应用需求,本文发展了一种半自动化的相对辐射归一化方法。将多源遥感影像的相对辐射归一化分为传感器辐射校正与针对光照等外部因素的辐射归一化2个过程。首先,基于晴空影像,采用分类回归的方式获取传感器辐射校正系数;然后,利用样本传递再分类的方法实现多源影像的半自动分类和传感器辐射偏差校正;最后,基于NDVI差值直方图和类别约束的PIFs自动选取方法,实现影像的相对辐射归一化。采用准同期的GF1-WVF1和Landsat8-OLI影像以及多源时序影像对方法进行了验证,结果表明,本文方法可以对传感器间的辐射偏差进行有效纠正,并在整体上获得比传统方法更好的辐射归一化精度;同时,多源时序影像的辐射校正结果也表明,本文方法能够有效地消除时序影像间的辐射特征波动,使植被等地类的季相变化信息得到更准确地表达,为多源时序影像的协同利用提供了借鉴方法。

关键词: 时间序列, 多源影像, 辐射归一化, 半自动分类

Abstract:

In order to meet the demand for quantitative information extraction from multi-sources and multi-temporal satellite data, a semi-automatically relative radiometric normalization approach was developed in this paper. The relative radiometric normalization procedure of multi-sources image is divided into two parts: the first one is sensors’ radiometric correction and the other is the normalization of radiometric discrepancy caused by external factors such as the changes of illumination. Firstly, the radiometric correction coefficients of sensors were obtained by the classes-specified regression method based on clear-sky images. Secondly, by applying the sample transferring method, the multi-sources images were semi-automatically classified, and the radiometric deviations of the corresponding sensors were adjusted. Lastly, based on the images’ classification results, the PIFs were automatically chosen by combining the NDVI difference histogram with the class restraint, and the relative radiometric normalization of images was thereby accomplished. A pair of quasi-synchronous GF1-WVF1 and Landsat 8 images, and a set of multi-sources and time-series images were adopted to demonstrate the validity of the presented approach. The results showed that, this method can effectively correct sensors’ radiometric discrepancy and achieved a higher radiometric normalization accuracy when compared with the traditional method.Meanwhile, the results from time-series dataset also demonstrated that this method can effectively reduce the fluctuation of radiation features between time-series images, and the seasonal changes of vegetation were therefore accurately presented. It can thereby offer a reference to the synergic utilization of multi-sources and time-series images.

Key words: time series, multi-sources image, radiometric normalization, semi-automatic classification