模拟困难样本的Mask R-CNN滑坡分割识别

姜万冬; 席江波; 李振洪; 丁明涛; 杨立功; 谢大帅

doi:10.13203/j.whugis20200692

模拟困难样本的Mask R-CNN滑坡分割识别

doi: 10.13203/j.whugis20200692

姜万冬^1,2,
席江波^1,2,3,
李振洪^1,2,3,
丁明涛^1,2,3,
杨立功¹,
谢大帅^1,2

1 长安大学地质工程与测绘学院, 陕西西安, 710054;
2 长安大学地学与卫星大数据研究中心, 陕西西安, 710054;
3 西部矿产资源与地质工程教育部重点实验室, 陕西西安, 710054

基金项目:

国家自然科学基金重大项目（41941019），国家重点研发计划（2018YFC1504805），国家自然科学基金（61806022，41874005），中央高校基本科研业务费专项资金（300102260301/087，300102260404/087，300102269103，300102269304和300102269205），地理信息国家重点实验室开放基金（SKLGIE2018-M-3-4）。

详细信息

作者简介:
姜万冬,硕士,主要研究方向为遥感影像地质灾害智能解译,CHD_jwd@126.com*

Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples

1 College of Geological Engineering and Geomantics, Chang'an University, Xi'an, Shaanxi 710054, China;
2 Big Data Center for Geosciences and Satellites, Chang'an University, Xi'an, Shaanxi 710054, China;
3 Key Laboratory of Western China's Mineral Resources and Geological Engineering, Ministry of Education, Xi'an, Shaanxi 710054, China

Funds:

Major Program of the National Natural Science Foundation of China (41941019), The National Key Research and Development Program of China (2018YFC1504805), The National Natural Science Foundation of China(61806022, 41874005), The Fundamental Research Funds for the Central Universities (300102260301/087, 300102260404/087, 300102269103, 300102269304, and 300102269205), the State Key Laboratory of Geographic Information (SKLGIE2018-M-3-4).

摘要: 随着人工智能的发展，利用高分影像进行滑坡等地质灾害识别逐渐成为研究热点。滑坡目视解译需依赖专家经验，传统滑坡自动识别方法又易将滑坡和裸地、道路等地物混淆。针对以上问题，提出基于模拟困难样本的Mask R-CNN（mask region- based convolutionalnetwork）滑坡提取方法。在现有样本的基础上，利用滑坡的形状、颜色、纹理等特征模拟更为复杂的滑坡背景，进行困难样本挖掘增强，并将得到的困难样本输入Mask R-CNN网络进行滑坡精细检测分割。在实际研究区域中，由于滑坡数量有限，因此在频率域进行小样本学习，在减少数据需求的同时，保证分割识别的准确度。在贵州毕节的实验结果表明，基于模拟困难样本的Mask R-CNN方法检测精度为94.0%，像素分割平均准确率为90.3%，可实现低虚警率下的高性能检测分割；采用频率域学习，在一半数据输入量的情况下，模型检测精度仍可得到提升；并利用天水地区的滑坡区域进行实际验证，进一步证明了所提方法的有效性。
- 深度学习 /
- 滑坡提取 /
- Mask R-CNN /
- 困难样本 /
- 频率域
Abstract: With the advance in artificial intelligence, using high-resolution images to detect geological hazards has gradually become a research hotspot. Visual interpretation of landslides heavily relies on expert experience, and conventional automatic landslide detection approaches are sensitive to the presence of bare land, roads and other ground objects. To address these, a Mask R-CNN with simulated hard samples is presented in this paper for landslide detection and segmentation. Based on existing landslide samples, hard landslide samples are simulated by utilizing the shapes, colors, textures, and other characteristics of landslides to make each of the samples with a more complicated background. The original imagery and simulated hard samples are then fed into the Mask R-CNN for landslide detection and segmentation. Since the number of landslides is often limited in reality, small sample learning in the frequency domain is also presented in this paper to reduce the number of input samples while ensuring the accuracy of detection and segmentation. The experimental results in Bijie, Guizhou Province, showed that the detection and the average pixel segmentation accuracies of the proposed Mask R-CNN method with simulated hard samples are 94.0% and 90.3%, respectively. It is seen that the proposed method has high performance on landslide detection and segmentation with low false alarm rates. In addition, the performance of the proposed small-sample-based learning method in frequency domain can be improved even with a half of the data input. The effectiveness of the proposed Mask R-CNN method is further proved by the successful detection of Tianshui landslides in Gansu Province.
- Deep learning /
- Landslide detection /
- Mask R-CNN /
- Hard samples /
- Frequency domain

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模拟困难样本的Mask R-CNN滑坡分割识别

doi: 10.13203/j.whugis20200692

作者简介:
姜万冬,硕士,主要研究方向为遥感影像地质灾害智能解译,CHD_jwd@126.com*

Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples

计量

模拟困难样本的Mask R-CNN滑坡分割识别

doi: 10.13203/j.whugis20200692

1 长安大学地质工程与测绘学院, 陕西西安, 710054;

2 长安大学地学与卫星大数据研究中心, 陕西西安, 710054;

3 西部矿产资源与地质工程教育部重点实验室, 陕西西安, 710054

作者简介:
姜万冬,硕士,主要研究方向为遥感影像地质灾害智能解译,CHD_jwd@126.com*

English Abstract

Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples

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留言板

模拟困难样本的Mask R-CNN滑坡分割识别

doi: 10.13203/j.whugis20200692

作者简介: 姜万冬,硕士,主要研究方向为遥感影像地质灾害智能解译,CHD_jwd@126.com*

Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples

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出版历程

模拟困难样本的Mask R-CNN滑坡分割识别

doi: 10.13203/j.whugis20200692

1 长安大学地质工程与测绘学院, 陕西 西安, 710054; 2 长安大学地学与卫星大数据研究中心, 陕西 西安, 710054; 3 西部矿产资源与地质工程教育部重点实验室, 陕西 西安, 710054

作者简介: 姜万冬,硕士,主要研究方向为遥感影像地质灾害智能解译,CHD_jwd@126.com*

English Abstract

Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples

全文HTML

目录

作者简介:
姜万冬,硕士,主要研究方向为遥感影像地质灾害智能解译,CHD_jwd@126.com*

1 长安大学地质工程与测绘学院, 陕西西安, 710054;

2 长安大学地学与卫星大数据研究中心, 陕西西安, 710054;

3 西部矿产资源与地质工程教育部重点实验室, 陕西西安, 710054

作者简介:
姜万冬,硕士,主要研究方向为遥感影像地质灾害智能解译,CHD_jwd@126.com*