| 基于分层极限学习机影像转换的多源影像变化检测方法 |
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| 引用本文： | 韩特,汤玉奇,邹滨,冯徽徽,张芳艳.基于分层极限学习机影像转换的多源影像变化检测方法[J].地球信息科学,2022,24(11):2212-2224. |
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| 作者姓名： | 韩特 汤玉奇 邹滨 冯徽徽 张芳艳 |
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| 作者单位： | 1.中南大学地球科学与信息物理学院，长沙 4100832.中南大学 有色金属成矿预测与地质环境监测教育部重点实验室， 长沙 4100833.宁夏大学智能工程与技术学院，中卫 755000 |
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| 基金项目： | 国家重点研发计划项目(2018YFA06055);国家自然科学基金项目(41971313);湖南省自然科学基金项目(2019JJ40372) |
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| 摘 要： | 为提高现有多源影像无监督变化检测方法存在的检测结果易受噪声影响和计算效率低等问题，本文提出了一种基于分层极限学习机影像转换的多源影像变化检测方法。分层极限学习机（Hierarchical Extreme Learning Machine， HELM）通过多层前向编码获得丰富的特征表示，且当特征提取完成即可确定网络参数。本文方法首先通过对合成孔径雷达（Synthetic Aperture Radar， SAR）影像进行对数转换，以获得与光学影像相同的影像噪声分布，并利用影像平滑减少影像噪声对变化检测结果的影响；然后分别对多源影像进行聚类分析，通过对比两时相影像的聚类图获得初始变化检测图，选取初始变化检测图中的未变化区域的像元作为初始训练样本，构建训练样本修正模型修正初始训练样本以提高训练样本的准确性；引入HELM以实现多源影像特征空间转换，获取多时相空间转换影像，提高了算法效率；最后通过对比原始影像和多时相空间转换影像获取变化信息。两组多源影像(Google Earth和哨兵1号影像)的实验结果表明：与现有方法相比，本文方法的Kappa系数分别至少提高了6.19%和8.94%，证明了本文方法对多源影像变化检测的有效性。
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| 关 键 词： | 分层极限学习机 影像转换 多源影像 变化检测 噪声分布 训练样本 修正模型 |
| 收稿时间： | 2022-03-02 |
Heterogeneous Images Change Detection Method based on Hierarchical Extreme Learning Machine Image Transformation |
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| HAN Te,TANG Yuqi,ZOU Bin,FENG Huihui,ZHANG Fangyan.Heterogeneous Images Change Detection Method based on Hierarchical Extreme Learning Machine Image Transformation[J].Geo-information Science,2022,24(11):2212-2224. |
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| Authors: | HAN Te TANG Yuqi ZOU Bin FENG Huihui ZHANG Fangyan |
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| Institution: | 1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China3. School of Intelligent Engineering and Technology, Ningxia University, Zhongwei 755000, China |
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| Abstract: | Due to the complementary information between different imaging mechanisms, heterogeneous image change detection is a challenging and hot topic compared to homogeneous image change detection. Its application is widespread, especially in emergency situations caused by natural disasters. To address the limitations of existing methods such as susceptibility to noise, manually selecting samples, and time-consuming computation, we propose a change detection method for heterogeneous images based on image transformation using Hierarchical Extreme Learning Machine (HELM). In our method, a HELM transformation model between heterogeneous images is constructed, which transforms the image features of one image into the feature space of the other one. Consequently, the transformed multi-temporal images could be comparable. Specifically, first, the logarithmic transformation of SAR images is carried out to obtain the same noise distribution model with the optical image. These heterogeneous images are smoothed to reduce the impact of image noise. Then, through image segmentation, the unchanged areas are selected as training samples. And a correction model for training samples is constructed to avoid manual selection of samples and improve the accuracy of image transformation. Subsequently, the corrected training samples are used to train the HELM to obtain the multi-temporal transformation images, which avoids the parameter adjustment of neural networks. Finally, the changes could be extracted by comparing the transformed multi-temporal images. To prove the effectiveness of the method, two sets of heterogeneous images (Google Earth and Sentinel 1 images) are used for experimental validation in this paper. The results show that the kappa coefficients of the method for the two data sets are improved by 6.19% and 8.94%, respectively, compared with the existing methods, which proves the effectiveness of the proposed method. |
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| Keywords: | hierarchical extreme learning machine image transformation heterogeneous images change detection noise distribution training samples correction model |
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