Spectral-Spatial Weighted Kernel Manifold Embedded Distribution Alignment for Remote Sensing Image Classification

被引:68
作者
Dong, Yanni [1 ,2 ,3 ]
Liang, Tianyang [4 ]
Zhang, Yuxiang [4 ]
Du, Bo [5 ]
机构
[1] China Univ Geosci, Inst Geophys & Geomat, Hubei Subsurface Multiscale Imaging Key Lab, Wuhan 430074, Peoples R China
[2] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[3] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China
[4] China Univ Geosci, Inst Geophys & Geomat, Wuhan 430074, Peoples R China
[5] Wuhan Univ, Sch Comp Sci, Wuhan 430079, Peoples R China
基金
中国国家自然科学基金;
关键词
Remote sensing; Kernel; Manifolds; Distortion; Support vector machines; Principal component analysis; Learning systems; Classification; Grassmann manifold; remote sensing; spatial and spectral information; transfer learning; weighted kernel; DOMAIN-ADAPTATION; NEURAL-NETWORKS;
D O I
10.1109/TCYB.2020.3004263
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Feature distortions of data are a typical problem in remote sensing image classification, especially in the area of transfer learning. In addition, many transfer learning-based methods only focus on spectral information and fail to utilize spatial information of remote sensing images. To tackle these problems, we propose spectral-spatial weighted kernel manifold embedded distribution alignment (SSWK-MEDA) for remote sensing image classification. The proposed method applies a novel spatial information filter to effectively use similarity between nearby sample pixels and avoid the influence of nonsample pixels. Then, a complex kernel combining spatial kernel and spectral kernel with different weights is constructed to adaptively balance the relative importance of spectral and spatial information of the remote sensing image. Finally, we utilize the geometric structure of features in manifold space to solve the problem of feature distortions of remote sensing data in transfer learning scenarios. SSWK-MEDA provides a novel approach for the combination of transfer learning and remote sensing image characteristics. Extensive experiments have demonstrated that the proposed method is more effective than several state-of-the-art methods.
引用
收藏
页码:3185 / 3197
页数:13
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