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Deep Feature-Based Multitask Joint Sparse Representation for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2019.2950095.png)
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Abstract
En 中文
Deep multiscale features extracted from diverse perspectives present a more powerful ability than shallow ones for hyperspectral image (HIS) classification. In this letter, we proposed a deep feature-based multitask joint sparse representation (D-MJSR) method. First, filter banks transferred from the pretrained VGG16 network are utilized to extract multiscale features of HSI. Then, features from each scale layer are respectively and collaboratively fused with the raw spectral feature and then upsampled by bilinear interpolation to reach the input size. Finally, with the advantages of feature distribution at different scales, JSR under multitask dictionaries is introduced to achieve the final classification, where samples are represented by dictionaries with different scale spaces independently, and neighborhood samples in each scale are represented by the same atoms wherever possible. We evaluate the proposed method D-MJSR by two public hyperspectral data sets quantitatively. Compared with existing feature extraction and SR-based methods, our method presents some significant improvement in classification accuracy.
Keywords:
Feature extraction
Data mining
Training
Hyperspectral imaging
Task analysis
Dictionaries
Convolutional neural network (CNN)
hyperspectral images classification (HSIC)
joint sparse representation (JSR)
multitask learning
transfer learning
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