曹汉瑞, 王妍, 李熙, 胡申森, 邱实, 魏英策. VIIRS夜光影像中农田火像素识别方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230256
引用本文: 曹汉瑞, 王妍, 李熙, 胡申森, 邱实, 魏英策. VIIRS夜光影像中农田火像素识别方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230256
CAO Hanrui, WANG Yan, LI Xi, HU Shensen, QIU Shi, WEI Yingce. Detecting Farmland Fire in VIIRS Night-time Light Images[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230256
Citation: CAO Hanrui, WANG Yan, LI Xi, HU Shensen, QIU Shi, WEI Yingce. Detecting Farmland Fire in VIIRS Night-time Light Images[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230256

VIIRS夜光影像中农田火像素识别方法

Detecting Farmland Fire in VIIRS Night-time Light Images

  • 摘要: 夜光遥感影像已被广泛应用于人类活动、社会经济等方面的研究。南部非洲的夜光影像上存在因烧田产生的农田火像素,这些像素易与城镇灯光像素混淆,干扰了夜光影像在社会经济评估中的应用。以南部非洲的10个大陆国家为研究区域,基于NPP/VIIRS数据生产的Black Marble产品,构建了夜光辐亮度时间序列的三个特征,然后采用随机森林分类方法将夜光影像中的像素分为农田火像素、稳定灯光像素和全黑像素。最后采用分层随机抽样,以时间序列夜光辐亮度数据、高分辨率卫星影像和地表覆盖数据对像素分类结果进行精度检验。结果表明,像素分类的总体精度为91.2%,平均生产者精度为91.9%,平均用户精度为91.0%,其中农田火像素分类的生产者精度和用户精度分别为86.4%和92.6%。本研究提出的方法可在后续应用中滤除Black Marble产品中的农田火像素,以提升夜光影像对非洲社会经济的评估精度。

     

    Abstract: Objectives: Night-time light images have been widely used in human activity analysis, social economy estimation and other aspects. However, on night-time light images, farmland fire pixels caused by burning fields are easily confused with the urban light pixels, which interferes with the socioeconomic assessments using night-time light images. Based on the radiance time series characteristics of pixels, the farmland fire pixels on night-time light images can be identified through the random forest method. Methods: In this study, 10 continental countries in southern Africa were taken as the research area. Based on Black Marble product produced by NPP/VIIRS data, three characteristics of the time series of night-time light radiance were constructed, and the random forest classification method was used to divide the pixels into farmland fire pixels, stable light pixels, and black pixels. Results: In this study, stratified random sampling was used to manually test the classification accuracy, using time series of nighttime light data, high resolution satellite images and land cover data. Results showed that the overall accuracy of pixel classification was 91.2%, the average producer accuracy was 91.9%, and the average user accuracy was 91.0%. Among them, the producer accuracy and user accuracy of farmland fire pixel classification were 86.4% and 92.6%, respectively. Conclusions: The random forest classification method was used to classify the pixels on night-time light images into farmland fire pixels, stable light pixels and black pixels with high accuracy. The method proposed in this study can be used to filter out farmland fire pixels in Black Marble products, so as to improve the evaluation accuracy for African social economy using night-light images.

     

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