A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
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DOI：
10.3390/rs8050403
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Lei Lin;Yu Meng;Anzhi Yue;Yuan Yuan-Yuan;Xiaoyi Liu;Jingbo Chen;Mengmeng Zhang;Jiansheng Chen
中科院分区：
文献类型：
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作者：
Lei Lin;Yu Meng;Anzhi Yue;Yuan Yuan-Yuan;Xiaoyi Liu;Jingbo Chen;Mengmeng Zhang;Jiansheng Chen
基于多时相遥感数据的火灾探测是一个非常活跃的研究领域。然而，多时相遥感图像的检测过程通常是复杂的，因为空间和时间的变化。提出了一种基于时空模型（STM）的森林火灾检测方法。在STM中，被检测像素与其相邻像素之间的强相关性被考虑，这可以减轻空间异质性对背景强度预测的不利影响。空间上下文信息和时间信息的集成使其成为一个更强大的异常检测模型。该算法应用于2009年在伊南河森林，黑龙江省，中国，使用两个月的HJ-1B红外相机传感器（IRS）图像的森林火灾。实验结果表明，本文提出的算法能够有效地表征多时相遥感数据中的时空信息，并且STM检测方法比优化的上下文算法具有更高的检测精度。
Fire detection based on multi-temporal remote sensing data is an active research field. However, multi-temporal detection processes are usually complicated because of the spatial and temporal variability of remote sensing imagery. This paper presents a spatio-temporal model (STM) based forest fire detection method that uses multiple images of the inspected scene. In STM, the strong correlation between an inspected pixel and its neighboring pixels is considered, which can mitigate adverse impacts of spatial heterogeneity on background intensity predictions. The integration of spatial contextual information and temporal information makes it a more robust model for anomaly detection. The proposed algorithm was applied to a forest fire in 2009 in the Yinanhe forest, Heilongjiang province, China, using two-month HJ-1B infrared camera sensor (IRS) images. A comparison of detection results demonstrate that the proposed algorithm described in this paper are useful to represent the spatio-temporal information contained in multi-temporal remotely sensed data, and the STM detection method can be used to obtain a higher detection accuracy than the optimized contextual algorithm.