A Two-Stream Unified Interpretation Network for Heterogeneous Remote Sensing Images Classification
- Resource Type
- Conference
- Authors
- Wang, Yan; He, Chu; Xiong, Dehui; Tu, Mingxia
- Source
- IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2018 - 2018 IEEE International. :1784-1787 Jul, 2018
- Subject
- Aerospace
Computing and Processing
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Signal Processing and Analysis
Radio frequency
Streaming media
Data models
Sparse matrices
Remote sensing
Support vector machines
Task analysis
Remote sensing image classification
deep learning
graph embedding
sparse and low-rank representations
fully convolutional network
subspace learning
- Language
- ISSN
- 2153-7003
The conventional studies on different types of remote sensing (RS) images classifications are conducted separately. Thanks to the powerful potential of deep learning to automatically learn features from data, exploring a unified method is possible. Moreover, recent research shows that sparse and low-rank representations can convey valuable information for patterns classification. Therefore, this paper presents a two-stream heterogeneous RS images unified interpretation network (HRSIUI-Net). One stream is to transfer the pre-trained fully convolutional network to learn deep multi-scale spatial features of RS data. The other stream is to employ a subspace learning based on graph embedding to learn the sparse and low-rank subspace representations of high-dimensional features. And then, two streams of learned subspace features are integrated for classification combined with an SVM. The experimental results on two typical RS data indicate that HRSIUI-Net can achieve competitive performance.