应用神经网络进行卫星遥感图像的热异常信息提取

概要介绍了影响地表温度的几个主要因素,在此基础上提出了对TM卫星图像进行热异常信息提取的基本方法和步骤,其中又着重分析了神经网络在建立热异常信息提取的数学模型方面的应用。针对神经网络的该种应用特点,应用了样本集批处理和加入衰减的动量因子两种BP神经网络的改进办法,使神经网络对于训练样本集的学习能力得到了明显提高。把应用神经网络进行热异常信息提取后的TM卫星图像与基于航空遥感获得的图像进行比较表明,这里提出的热异常信息提取方法可以应用于煤层自燃的探测,而且在成本和检测周期等方面均有很大的优势。

Detecting the Coal Fires on Landsat TM Thermal IR Images with Neural Network

Coal fires are widely prevalent in the north of China. They have already caused huge losses in resources and pose a serious environmental problem. To monitor and extinguish coal fires, the first step is to detect their location and scale. Because of the huge amounts of heat energy released by coal fires, the resulting thermal anomalies can be detected by using thermal infrared remote sensing technology. On nocturnal aerial images it is relatively easy to discern coal fires, because the effect of solar radiation is insignificant. However, nocturnal aerial images are not available as often as Landsat TM daytime images for such a large area as the north of China. In this paper, we first give a briefing of the basic principle in reducing solar radiation on TM thermal IR image. Then, neural network is used to set up a mathematical model of ground temperature. In view of the special character of artificial neural network used in this application, we offer the batch learning approach and adjust active momental factor. The result achieved by reducing solar radiation on TM thermal IR image is as good as airborne nighttime thermal infrared image for detecting coal fires. So this method is very practical and greatly economizes the cost of aerial remote sensing image.