Paper Info
Title
A Superpixel-Based Relational Auto-Encoder for Feature Extraction of Hyperspectral Images
Abstract
Filter banks transferred from a pre-trained deep convolutional network exhibit significant performance in heightening the inter-class separability for hyperspectral image feature extraction, but weakening the intra-class consistency simultaneously. In this paper, we propose a new superpixel-based relational auto-encoder for cohesive spectral-spatial feature learning. Firstly, multiscale local spatial information and global semantic features of hyperspectral images are extracted by filter banks transferred from the pre-trained VGG-16. Meanwhile, we utilize superpixel segmentation to construct the low-dimensional manifold embedded in the spectral domain. Then, representational consistency constraint among each superpixel is added in the objective function of sparse auto-encoder, which iteratively assist and supervisedly learn hidden representation of deep spatial feature with greater cohesiveness. Superpixel-based local consistency constraint in this work not only reduces the computational complexity, but builds the neighborhood relationships adaptively. The final feature extraction is accomplished by collaborative encoder of spectral-spatial feature and weighting fusion of multiscale features. A large number of experimental results demonstrate that our proposed method achieves expected results in discriminant feature extraction and has certain advantages over some existing methods, especially on extremely limited sample conditions.
Year
DOI
Venue
2019
10.3390/rs11202454
REMOTE SENSING
Keywords
DocType
Volume
hyperspectral images classification (HSIC),convolutional neural network (CNN),relational auto-encoder (RAE),transfer learning,superpixel,feature fusion
Journal
11
Issue
Citations 
PageRank 
20
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Miaomiao Liang1113.20
Licheng Jiao25698475.84
Zhe Meng300.34