面向对象的多尺度加权联合稀疏表示的高空间分辨率遥感影像分类

doi:10.11947/j.AGCS.2022.20190290

测绘学报 ›› 2022, Vol. 51 ›› Issue (2): 224-237.doi: 10.11947/j.AGCS.2022.20190290

面向对象的多尺度加权联合稀疏表示的高空间分辨率遥感影像分类

洪亮^1,2,3, 冯亚飞⁴, 彭双云^1,2,3, 楚森森^1,5

1. 云南师范大学地理学部, 云南昆明 650500;
2. 西部资源环境地理信息技术教育部工程研究中心, 云南昆明 650500;
3. 云南省地理空间信息技术工程技术研究中心, 云南昆明 650500;
4. 昆明市信息中心, 云南昆明 650506;
5. 南京大学地理信息科学系, 江苏南京 210023

收稿日期:2019-08-02 修回日期:2021-09-30 发布日期:2022-02-28
通讯作者: 楚森森 E-mail:chusensen@163.com
作者简介:洪亮(1981-),男,博士,教授,研究方向为高分辨率遥感影像智能处理及其应用。E-mail:hongliang20433@hotmail.com
基金资助:
国家自然科学基金（41861048；41971369）；云南省中青年学术技术带头人后备人才项目（202105AC160059）；云南省基础研究专项重点项目（202001AS070032）

Classification of high spatial resolution remote sensing imagery based on object-oriented multi-scale weighted sparse representation

HONG Liang^1,2,3, FENG Yafei⁴, PENG Shuangyun^1,2,3, CHU Sensen^1,5

1. Faculty of Geography, Yunnan Normal University, Kunming 650500, China;
2. GIS Technology Research Center of Resource and Environment in Western China of Ministry of Education, Yunnan Normal University, Kunming 650500, China;
3. Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China;
4. Kunming Information Center, Kunming 650506, China;
5. Department of Geographic information Science, Nanjing University, Nanjing 210023, China

Received:2019-08-02 Revised:2021-09-30 Published:2022-02-28
Supported by:
The National Natural Science Foundation of China (Nos. 41861048; 41971369); Yunnan Province Young Academic and Technical Leaders Reserve Talent Project (No. 202105AC160059);Yunnan Province Basic Research Special Key Project (No. 202001AS070032)

摘要/Abstract

摘要： 针对高空间分辨率遥感影像中的地物具有多尺度特性，以及各个尺度的对象特征对地物分类精度的影响具有较强的尺度效性，并结合面向对象影像分析方法和多尺度联合稀疏表示方法在高空间分辨率遥感影像分类中的各自优点，提出了一种面向对象的多尺度加权稀疏表示的高空间分辨率遥感影像分类算法。首先，采用多尺度分割算法获得多尺度分割结果并提取对象的多尺度特征；然后，根据影像对象的多尺度分割质量测度计算各尺度的对象权重，构建面向对象的多尺度加权联合稀疏表示模型；最后，采用2个国产GF-2高空间分辨率遥感数据集和1个高光谱-高空间分辨率航空遥感数据集（WashingtonD.C.数据）验证该算法的有效性。试验结果表明，与SVM、像素级稀疏表示、单尺度和多尺度对象级稀疏表示和深度学习等算法相比较，本文算法获得了较高的OA和Kappa分类精度，提高了各个尺度地物的分类精度，有效抑止了地物分类结果中的椒盐噪声现象，同时保持大尺度地物的区域性和小尺度地物的细节信息。

关键词: 高空间分辨率遥感影像, 面向对象, 多尺度分割, 对象莫兰指数, 加权联合稀疏表示

Abstract: In this paper, according to the multi-scale advantage for high spatial resolution remote sensing imagery and the influence difference among multi-scale objects for classification, the objected-oriented multi-scale weighted sparse representation classification algorithm is proposed by taking the advantages of object-based image analysis method and sparse representation classification algorithm. Firstly, the multi-scale segmentation results are obtained and the multi-scale features are extracted by the multi-scale segmentation algorithm; secondly, the object weights in each scale are computed according to multi-scale segmentation quality measure, and the objected-oriented multi-scale weighted sparse representation model is constructed; finally, the two domestic GF-2 high spatial resolution remote sensing images and one high-spatial and spectral resolution dataset (Washington D.C. data) were adopted to verify the proposed algorithm. The experiment results show that the proposed algorithm can obtain the highest classification accuracy with OA and Kappa,efficiently improve classification accuracy at each scale objects, reduce salt and pepper noise in the classification results, and respectively maintain the regional integrity in the large scale objects and the details in the small scale objects comparing with the traditional SVM, pixel sparse representation,single scale and multi-scale sparse representation and object-based deep learning methods.

Key words: high spatial resolution remote sensing imagery, object-oriented, multi-scale segmentation, object-based Local Moran's I, weighted joined sparse representation

中图分类号:

P227

洪亮, 冯亚飞, 彭双云, 楚森森. 面向对象的多尺度加权联合稀疏表示的高空间分辨率遥感影像分类[J]. 测绘学报, 2022, 51(2): 224-237.

HONG Liang, FENG Yafei, PENG Shuangyun, CHU Sensen. Classification of high spatial resolution remote sensing imagery based on object-oriented multi-scale weighted sparse representation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 224-237.

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面向对象的多尺度加权联合稀疏表示的高空间分辨率遥感影像分类

Classification of high spatial resolution remote sensing imagery based on object-oriented multi-scale weighted sparse representation

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