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Deep Tensor Factorization for Hyperspectral Image Classification
- Source :
- IGARSS
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- High-dimensional spectral feature and limited training samples have caused a range of difficulties for hyperspectral image (HSI) classification. Feature extraction is effective to tackle this problem. Specifically, tensor factorization is superior to some prominent methods such as principle component analysis (PCA) and non-negative matrix factorization (NMF) because it takes spatial information into consideration. Recently, deep learning has gotten more and more attention for efficiently extracting hierarchical features for various tasks. In this paper, we propose a novel feature extraction method, deep tensor factorization (DTF), to extract hierarchical and meaningful features from observed HSI. This method takes advantage of tensor in representing HSI and the merits of convolutional neural network (CNN) in hierarchical feature extraction. Specifically, a convolution operation is firstly applied in the spectral dimension of HSI to suppress the effect of noise. Then, the convolved HSI is fed into tensor factorization to learn a low rank representation of data. After that, the above two process are repeated to learn a hierarchical representation of HSI. Experimental results on two real hyperspectral data sets show the superiority of the proposed method.
- Subjects :
- Computer science
business.industry
Deep learning
Feature extraction
0211 other engineering and technologies
Hyperspectral imaging
Pattern recognition
02 engineering and technology
Convolutional neural network
Convolution
Matrix decomposition
Non-negative matrix factorization
Kernel (linear algebra)
Kernel (image processing)
Principal component analysis
0202 electrical engineering, electronic engineering, information engineering
Physics::Accelerator Physics
020201 artificial intelligence & image processing
Tensor
Artificial intelligence
business
021101 geological & geomatics engineering
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
- Accession number :
- edsair.doi...........d02cea4d5689d40a1967519177dab52d