自适应边缘阈值法的煤火探测研究

    Coal fires detection study using adaptive-edge threshold algorithm

    • 摘要: 为了探测内蒙古乌达煤田的煤火位置,针对国产CBERS-04卫星的热红外波段提出一种自适应边缘阈值算法。该算法通过高斯滤波算法过滤热图像噪声,利用Sobel算子和提取的高温区域生成高温边缘,将高温边缘像素温度值的平均值作为温度分割阈值识别并提取煤火区。通过外业调查的火区验证提取的火区,发现二者的位置重叠度高达81.3%。对比同一日夜晚ASTER卫星和CBERS-04卫星所提取火区发现二者的位置重叠度为81.0%。这些结果均表明自适应边缘阈值煤火识别算法具有较高的煤火识别精度。

       

      Abstract: In order to detect positions of coal fires in Wuda coal field, Inner Mongolia, an adaptive-edge threshold algorithm (AETA) is proposed aiming at the thermal infrared band from domestic CBERS-04 remote sensing satellite.AETA first filters the noises of an image using Gaussian filter algorithm, generates high-temperature edge combined Sobel operator with extracted high-temperature regions, averages the pixel values located in high-temperature edge, and applies this mean of temperatures as temperature-segmented threshold to identify and extract coal fire areas.By comparing field-investigated fire areas with extracted fire areas, we find that the overlap ratio of both areas reaches 81.3%.Additionally, the two fire areas recognized by ASTER satellite and CBERS-04 satellite at night on the same day show the overlap ratio of both fire areas is 81.0%.These results demonstrate that AETA algorithm can provide the higher precision for coal fires detection.

       

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