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Scatter Matrix Based Domain Adaptation for Bi-Temporal Polarimetric SAR Images
- Source :
- Remote Sensing, Volume 12, Issue 4, Remote Sensing, Vol 12, Iss 4, p 658 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- Time series analysis (TSA) based on multi-temporal polarimetric synthetic aperture radar (PolSAR) images can deeply mine the scattering characteristics of objects in different stages and improve the interpretation effect, or help to extract the range of surface changes. However, as far as classification is concerned, it is difficult to directly generate the classification map for a new temporal image, by the use of conventional TSA or change detection methods. Once some labeled samples exist in historical temporal images, semi-supervised domain adaptation (DA) is able to use historical label information to infer the categories of pixels in the new image, which is a potential solution to the above problem. In this paper, a novel semi-supervised DA algorithm is proposed, which inherits the merits of maximum margin criterion and principal component analysis in the DA learning scenario. Using a kernel mapping function established on the statistical distribution of PolSAR data, the proposed algorithm aims to find an optimal subspace for eliminating domain influence and keeping the key information of bi-temporal images. Experiments on both UAVSAR and Radarsat-2 multi-temporal datasets show that, superior classification results with the average accuracy of about 80% can be obtained by a simple classifier trained with historical labeled samples in the learned low- dimensional subspaces.
- Subjects :
- 010504 meteorology & atmospheric sciences
Computer science
domain adaptation
Science
bi-temporal images
0211 other engineering and technologies
02 engineering and technology
transfer learning
01 natural sciences
Image (mathematics)
Scatter matrix
Margin (machine learning)
Classifier (linguistics)
021101 geological & geomatics engineering
0105 earth and related environmental sciences
graph embedding
Pixel
business.industry
scatter matrices
polarimetric SAR
Pattern recognition
reproducing kernel Hilbert spaces
Principal component analysis
dissimilarity measure
General Earth and Planetary Sciences
Artificial intelligence
business
Subspace topology
Change detection
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....fa1250ff08da05237385e5fe89708e0d
- Full Text :
- https://doi.org/10.3390/rs12040658