高分辨率遥感影像的深度学习变化检测方法

Abstract

为提升高分辨率遥感影像的变化检测精度,提出一种利用深度学习的变化检测方法.在预处理的基础上,利用顾及邻域信息的改进变化矢量分析算法和灰度共生矩阵算法获取影像间光谱和纹理变化,并通过设置自适应采样区间提取最可能的变化和未变化区域样本.构建并训练包含标签层的高斯伯努利深度限制玻尔兹曼机模型,以提取变化和未变化区域深层特征,从而有效辨别变化区域.通过WorldView-3与Pléiades-1影像的试验表明本文方法在变化检测精度方面优于对比方法.

A novel change detection method is proposed based on deep learning to improve the accuracy of change detection in very high spatial resolution remote sensing images.On the base of image pre-processing, spectral and texture changes are extracted by modified change vector analysis and grey level co-occurrence matrix respectively, both concerning spatial-contextual information.Most likely changed and unchanged pixel-pairs are obtained by an adaptive threshold for selecting the labeled samples.The proposed model based on Gaussian-Bernoulli deep Boltzmann machines with a label layer is built to learn high-level features and is trained for determining the change areas.Experimental results on WorldView-3 and Pléiades-1 show that the proposed method out performs the compared methods in the accuracy of change detection.