高分辨率遥感影像的深度学习变化检测方法

张鑫龙; 陈秀万; 李飞; 杨婷

doi:10.11947/j.AGCS.2017.20170036

测绘学报 >

2017 , Vol. 46 >Issue 8: 999 - 1008

DOI: https://doi.org/10.11947/j.AGCS.2017.20170036

摄影测量学与遥感

高分辨率遥感影像的深度学习变化检测方法

张鑫龙 ,
陈秀万 ,
李飞 ,
杨婷

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北京大学地球与空间科学学院, 北京 100871

张鑫龙(1990-),男,博士生,研究方向为摄影测量与遥感。E-mail:mtxinlong@126.com

收稿日期: 2017-01-18

修回日期: 2017-03-17

网络出版日期: 2017-09-01

基金资助

国家自然科学基金（41272366；41230634）

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Change Detection Method for High Resolution Remote Sensing Images Using Deep Learning

ZHANG Xinlong ,
CHEN Xiuwan ,
LI Fei ,
YANG Ting

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School of Earth and Space Sciences, Peking University, Beijing 100871, China

Received date: 2017-01-18

Revised date: 2017-03-17

Online published: 2017-09-01

Supported by

The National Natural Science Foundation of China (Nos.41272366;41230634)

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摘要

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。

关键词： 变化检测; 高分辨率遥感; 深度学习

本文引用格式

张鑫龙 , 陈秀万 , 李飞 , 杨婷 . 高分辨率遥感影像的深度学习变化检测方法[J]. 测绘学报, 2017 , 46(8) : 999 -1008 . DOI: 10.11947/j.AGCS.2017.20170036

Abstract

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.

Key words： change detection; high resolution remote sensing; deep learning

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