基于深度学习算法的卫星影像变化监测

遥感影像的变化检测是遥感应用研究的热点之一,在城市变化、环境监测、土地利用以及基础地理数据库更新等领域中有着广泛的应用.变化检测是从不同时期的遥感数据中定量分析和确定地表变化的特征和过程,具体工作是对同一地区不同时相的两幅或多幅图像进行分析,检测出其中的变化部分与未变化部分.本文提出了基于堆栈降噪自动编码器网络的变化检测方法,将应用于SAR (Synthetic Aperture Radar,合成孔径雷达)卫星图像变化检测的深度学习算法改进,使之适用于高分光学卫星图像,然后在孪生网络的结构上进行改进,提出了基于分支卷积神经网络的变化检测方法,最后设计算法去除了阴影干扰和噪声等伪变化,并在高分二号卫星中宁夏地区的实际生产数据影像上进行了测试,取得了不错的效果.

Satellite Image Change Monitoring Based on Deep Learning Algorithm

Remote sensing image change detection is one of the hotspots of remote sensing application research. It has been widely used in urban change, environmental monitoring, land use, and basic geographic database update. Change detection is the feature and process of quantitative analysis and determination of surface changes from remote sensing data in different periods. The specific work is to analyze two or more images of different phases in the same region, and to detect the changed parts and unchanged parts. In this study, a change detection method based on stack noise reduction automatic encoder network is proposed. The deep learning algorithm applied to SAR (Synthetic Aperture Radar) satellite image change detection is improved, which is suitable for high-resolution remote sensing satellite image, and then improved on the structure of twin network. A change detection method based on branch convolutional neural network is proposed. Finally, the design algorithm removes the false changes such as shadow interference and noise, and tests it on the actual production data image of the high-resolution satellite 2 (GF-2). It has achieved sound results.