多光谱图像水域变化检测方法研究

Water Change Detection for Multi-spectral Images

针对遥感图像中水域的变化检测问题,提出一种综合特征级和像素级变化检测的多光谱图像变化检测方法,用于检测多时相遥感图像中水域的变化情况;首先利用BP神经网络,选取输入层为5,选定隐含层为10,完成多光谱图像的水域有效分类,再根据多光谱图像的水域特征,通过HSV空间准确定位水域区域,最后利用差值法进行多光谱图像水域变化检测;实验结果表明,该方法提高了变化检测的自动化程度和变化检测效率,具有较高的检测精度。

A novel two--step change detection algorithm combining feature--level and pixel--level techniques is proposed to detect chan- ges of water in multi--temporal remote sensing images. Firstly, water areas in multi--spectral images should be accurately classified on the basis of BP neural network, the input layer is selected as 5, the hidden layer is selected as 10. Secondly, water areas should be accurately located by the water features in HSV space. At last, water change detection in multi--spectral images will be finished by the difference method. The experiment results show this method improves change detection efficiency and the degree of automation of change detection. The method has higher detection precision.