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An effective technique to identify a river's stage through satellite images by means of RBFNN

机译:通过RBFNN通过卫星图像识别河流阶段的有效技术

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Today, a significant role is played by satellite image processing in the research improvement of various subject of analysis such as Astronomy, Remote Sensing, GIS, Agriculture Monitoring and Disaster Management. Forecasting natural disasters so that necessary safety measures can be taken to safeguard the surroundings is the objective behind the utilisation of remote sensing images in most of the researches. A vital role is played by water resource analysis besides others, in these researches. Several methods are conventionally used for the analysis and computation of the level of water in water resources. In this paper, the stage of a river is predicted utilising satellite images of the river. Initially, in the pre-processing phase, the image is filtered and then converted to the LAB colour space for acute analysis. Subsequently, the segmentation process is carried out using the designed Radial Basis Function Neural Network (RBFNN) and then morphological operation is performed on the image. After that in the testing phase, the segmented image is analysed and the stage of the river is identified as either normal or flood or draught using the designed RBFNN.
机译:今天,卫星图像处理在诸如天文学,遥感,GIS,农业监测和灾难管理等各种分析主题的研究改进中发挥着重要作用。预测自然灾害以便采取必要的安全措施以保护环境是大多数研究中利用遥感图像的目的。在这些研究中,除其他外,水资源分析也起着至关重要的作用。通常使用几种方法来分析和计算水资源中的水位。在本文中,利用河流的卫星图像来预测河流的阶段。最初,在预处理阶段,将图像过滤,然后转换为LAB颜色空间进行急性分析。随后,使用设计的径向基函数神经网络(RBFNN)进行分割过程,然后对图像进行形态学运算。之后,在测试阶段,将对分割的图像进行分析,并使用设计的RBFNN将河流的阶段识别为正常,洪水还是吃水。

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