热红外遥感技术在钢铁去产能监测中的应用

Application of thermal infrared remote sensing in monitoring the steel overcapacity cutting

为了解决以往人工实地监察督导方式对钢铁企业监测耗时耗力等问题,文章提出基于卫星热红外传感器发现高温异常区,将传统的遥感解译与热红外异常值监测相结合,选用第一季度3—5月准同步数据,结合已有钢铁企业范围和同期高分辨率影像等数据分析钢铁企业的热红外阈值,根据热红外阈值及热异常分布情况提取出疑似钢铁企业/“地条钢”企业;根据中高分辨率数字正射影像建立解译标志,套合已有钢铁企业和“地条钢”图斑,对异常区域进行判定;最后,将已有成果作为检验实验精度的依据,对监测成果进行检验,形成钢铁去产能监测对比结果,综合检测正确率为88.15%。研究结果显示,Landsat810.6~11.19μm热红外波段范围能够有效监测到钢铁企业高温异常,在未来热红外遥感热异常监测中可选取该范围波段。研究旨在为钢铁去产能监测探索更加广泛的数据源,从而为今后可能出现的数据瓶颈问题、应急监测问题等提供解决问题的思路与方法,为指导项目生产和钢铁去产能遥感监测提供一定的借鉴。

The monitoring of iron and steel enterprises through manual field supervision is time-consuming and labor-intensive.To address this problem,this study proposed identifying the high-temperature anomalous areas based on satellite-carried thermal infrared sensors.Then,based on conventional remote sensing interpretation combined with thermal infrared anomaly monitoring and the quasi-synchronous data of March to May in the first quarter,as well as the scope of existing iron and steel enterprises and high-resolution images of the same period,this study extracted information on suspected iron and steel enterprises/low-quality steel enterprises according to the thermal infrared threshold and the thermal anomaly distribution.Subsequently,interpretation symbols were constructed according to the medium-to high-resolution digital orthophoto maps(DOMs),and anomaly areas were identified by overlapping the map spots of existing iron and steel enterprises/low-quality steel enterprises.Finally,the monitoring results of the new method were tested using existing project results,forming the monitoring comparison results of steel overcapacity cutting.As a result,the comprehensive detection accuracy was 88.15%.The results of this study show that the Landsat8 data with a thermal infrared band of 10.6~11.19μm can effectively monitor the high-temperature anomalies of iron and steel enterprises.Therefore,this band can be selected for future thermal anomaly monitoring based on thermal infrared remote sensing.This study is designed to explore more extensive data sources for monitoring steel overcapacity cutting and to provide approaches to solve the possible data bottlenecks and emergency monitoring problems.It can be used as a reference for guiding both project production and remote sensing monitoring of steel overcapacity cutting.