Very-high-resolution (VHR) images can provide abundant ground details and spatial geometric information. Change detection in multi-temporal VHR images plays a significant role in urban expansion and area internal change analysis. Nevertheless, traditional change detection methods can neither take full advantage of spatial context information nor cope with the complex internal heterogeneity of VHR images. In this paper, a powerful feature extraction model entitled multi-scale feature convolution unit (MFCU) is adopted for change detection in multi-temporal VHR images. MFCU can extract multi-scale spatial-spectral features in the same layer. Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively. For unsupervised change detection, an automatic pre-classification is implemented to obtain reliable training samples, then DSMS-CN fits the statistical distribution of changed and unchanged areas from selected training samples through MFCU modules and deep siamese architecture. For supervised change detection, the end-to-end deep fully convolutional network DSMS-FCN is trained in any size of multi-temporal VHR images, and directly outputs the binary change map. In addition, for the purpose of solving the inaccurate localization problem, the fully connected conditional random field (FC-CRF) is combined with DSMS-FCN to refine the results. The experimental results with challenging data sets confirm that the two proposed architectures perform better than the state-of-the-art methods.
翻译:甚高分辨率图像可以提供丰富的地面细节和空间几何信息。多时VHR图像的变化检测在城市扩张和地区内部变化分析中起着重要作用。然而,传统的变化检测方法既不能充分利用空间背景信息,也不能应对VHR图像复杂的内部异质性。在本文中,采用了一个名为多尺度特征共变单位(MFCU)的强大特征提取模型,用于多时VHR图像的变化检测。MFCU可以在同一层中提取多尺度的空间光谱特征。基于该单元,两个新型的Siame 共振图像在城市扩张和地区内部内部变化分析中起着重要作用。传统变化检测方法既称为深层次的SAM-CN,又称为深层次的多级共振动图像网络(DMS-CN)和深层次的多级全级全级全级共振动网络(DSMS-FCN),用于不受监督的变化检测。为了获取可靠的培训样本样本样本,自动进行分类前测试,然后是DS-CN-CN的升级,从而通过经过全面测试的CRF的精细化的模板结构结构进行更精确的精细化和不断升级的S-CRMS-CF结果。