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Automated classification of heat sources detected using SWIR remote sensing
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2021-08-13 , DOI: 10.1016/j.jag.2021.102491
Soushi Kato 1 , Hiroki Miyamoto 2 , Stefania Amici 3 , Atsushi Oda 1 , Hiroyuki Matsushita 1 , Ryosuke Nakamura 1
Affiliation  

The potential of shortwave infrared (SWIR) remote sensing to detect hotspots has been investigated using satellite data for decades. The hotspots detected by satellite SWIR sensors include very high-temperature heat sources such as wildfires, volcanoes, industrial activity, or open burning. This study proposes an automated classification method of heat source detected utilizing Landsat 8 and Sentinel-2 data. We created training data of heat sources via visual inspection of hotspots detected by Landsat 8. A scheme to classify heat sources for daytime data was developed by combining classification methods based on a Convolutional Neural Network (CNN) algorithm utilizing spatial features and a decision tree algorithm based on thematic land-cover information and our time series detection record. Validation work using 10,959 classification results corresponding to hotspots acquired from May 2017 to July 2019 indicated that the two classification results were in 79.7% agreement. For hotspots where the two classification schemes agreed, the classification was 97.9% accurate. Even when the results of the two classification schemes conflicted, either was correct in 73% of the samples. To improve the accuracy, the heat source category was re-allocated to the most probable category corresponding to the combination of the results from the two methods. Integrating the two approaches achieved an overall accuracy of 92.8%. In contrast, the overall accuracy for heat source classification during nighttime reached 79.3% because only the decision tree-based classification was applicable to limited available data. Comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) fire product revealed that, despite the limited data acquisition frequency of Landsat 8, regional tendencies in hotspot occurrence were qualitatively appropriate for an annual period on a global scale.



中文翻译:

使用 SWIR 遥感检测到的热源的自动分类

几十年来,人们一直在使用卫星数据研究短波红外 (SWIR) 遥感检测热点的潜力。卫星短波红外传感器检测到的热点包括非常高温的热源,如野火、火山、工业活动或露天焚烧。本研究提出了一种利用 Landsat 8 和 Sentinel-2 数据检测到的热源的自动分类方法。我们通过对 Landsat 8 检测到的热点进行目视检查来创建热源训练数据。通过结合基于卷积神经网络 (CNN) 算法的分类方法,利用空间特征和决策树算法,开发了一种对白天数据的热源进行分类的方案基于专题土地覆盖信息和我们的时间序列检测记录。验证工作使用 10, 2017年5月至2019年7月获取的热点对应的959个分类结果表明,两个分类结果的一致性为79.7%。对于两种分类方案一致的热点,分类准确率为 97.9%。即使两种分类方案的结果发生冲突,在 73% 的样本中还是正确的。为了提高准确性,根据两种方法的结果组合,将热源类别重新分配到最可能的类别。整合这两种方法实现了 92.8% 的整体准确率。相比之下,夜间热源分类的总体准确率达到了 79.3%,因为只有基于决策树的分类适用于有限的可用数据。

更新日期:2021-08-13
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